Stable Alluvial Channel Design

• Riahi-Madvar, H., Gholami, M., & Gharabaghi, B. (2022). Improved explicit formulation of bedload transport using a novel multi-level multi-model data-driven ensemble approach. https://doi.org/10.21203/rs.3.rs-2120777/v1
• Kazemian-Kale-Kale, A., Gholami, A., Rezaie-Balf, M., Mosavi, A., Sattar, A. A., Azimi, A. H.,  & Bonakdari, H. (2021). Uncertainty Assessment of Entropy-Based Circular Channel Shear Stress Prediction Models Using a Novel Method. Geosciences, 11(8), 308. https://doi.org/10.3390/geosciences11080308
• Ebtehaj, I., Bonakdari, H., Zaji, A. H., & Gharabaghi, B. (2021). Evolutionary optimization of neural network to predict sediment transport without sedimentation. Complex & Intelligent Systems, 7, 401-416. https://doi.org/10.1007/s40747-020-00213-9
• Bonakdari, H., Gholami, A., Sattar, A. M., & Gharabaghi, B. (2020). Development of robust evolutionary polynomial regression network in the estimation of stable alluvial channel dimensions. Geomorphology, 350, 106895. https://doi.org/10.1016/j.geomorph.2019.106895
• Kazemian-Kale-Kale, A., Bonakdari, H., Gholami, A., & Gharabaghi, B. (2020). The uncertainty of the Shannon entropy model for shear stress distribution in circular channels. International Journal of Sediment Research, 35(1), 57-68. https://doi.org/10.1016/j.ijsrc.2019.07.001
• Bonakdari, H., Qasem, S. N., Ebtehaj, I., Zaji, A. H., Gharabaghi, B., & Moazamnia, M. (2020). An expert system for predicting the velocity field in narrow open channel flows using self-adaptive extreme learning machines. Measurement, 151, 107202. https://doi.org/10.1016/j.measurement.2019.107202
• Gholami, A., Bonakdari, H., Mohammadian, M., Zaji, A. H., & Gharabaghi, B. (2019). Assessment of geomorphological bank evolution of the alluvial threshold rivers based on entropy concept parameters. Hydrological Sciences Journal, 64(7), 856-872. https://doi.org/10.1080/02626667.2019.1608995
• Gholami, A., Bonakdari, H., Samui, P., Mohammadian, M., & Gharabaghi, B. (2019). Predicting stable alluvial channel profiles using emotional artificial neural networks. Applied Soft Computing, 78, 420-437. https://doi.org/10.1016/j.asoc.2019.03.003
• Gholami, A., Bonakdari, H., Zeynoddin, M., Ebtehaj, I., Gharabaghi, B., & Khodashenas, S. R. (2019). Reliable method of determining stable threshold channel shape using experimental and gene expression programming techniques. Neural Computing and Applications, 31(10), 5799-5817. https://doi.org/10.1007/s00521-018-3411-7
• Gholami, A., Bonakdari, H., Ebtehaj, I., Gharabaghi, B., Khodashenas, S. R., Talesh, S. H. A., & Jamali, A. (2018). A methodological approach of predicting threshold channel bank profile by multi-objective evolutionary optimization of ANFIS. Engineering Geology, 239, 298-309. https://doi.org/10.1016/j.enggeo.2018.03.030
• Gholami, A., Bonakdari, H., Ebtehaj, I., Mohammadian, M., Gharabaghi, B., & Khodashenas, S. R. (2018). Uncertainty analysis of intelligent model of hybrid genetic algorithm and particle swarm optimization with ANFIS to predict threshold bank profile shape based on digital laser approach sensing. Measurement, 121, 294-303. https://doi.org/10.1016/j.measurement.2018.02.070
• Shaghaghi, S., Bonakdari, H., Gholami, A., Kisi, O., Shiri, J., Binns, A. D., & Gharabaghi, B. (2018). Stable alluvial channel design using evolutionary neural networks. Journal of Hydrology, 566, 770-782. https://doi.org/10.1016/j.jhydrol.2018.09.057
• Shaghaghi, S., Bonakdari, H., Gholami, A., Kisi, O., Binns, A., & Gharabaghi, B. (2018). Predicting the geometry of regime rivers using M5 model tree, multivariate adaptive regression splines and least square support vector regression methods. International Journal of River Basin Management, 1-20. https://doi.org/10.1080/15715124.2018.1546731
• Kazemian-Kale-Kale, A., Bonakdari, H., Gholami, A., Khozani, Z. S., Akhtari, A. A., & Gharabaghi, B. (2018). Uncertainty analysis of shear stress estimation in circular channels by Tsallis entropy. Physica A: Statistical Mechanics and its Applications, 510, 558-576. https://doi.org/10.1016/j.physa.2018.07.014

Stream Assessment and Restoration

• MacKenzie, K., Auger, S., Beitollahpour, S., & Gharabaghi, B. (2024). The Role of Stream Restoration in Mitigating Sediment and Phosphorous Loads in Urbanizing Watersheds. Water, 16(2), 363. https://doi.org/10.3390/w16020363
• Rocha, P. A. C., Santos, V. O., & Gharabaghi, B. (2023). New graph-based and transformer deep learning models for river dissolved oxygen forecasting. Environments, 10(12), 217. 10.3390/environments10120217
• MacKenzie, K. M., Singh, K., Binns, A. D., Whiteley, H. R., & Gharabaghi, B. (2022). Effects of urbanization on stream flow, sediment, and phosphorous regime. Journal of Hydrology, 612, 128283. https://doi.org/10.1016/j.jhydrol.2022.128283
• Aredah, A. S., Ertugrul, O. F., Sattar, A. A., Bonakdari, H., & Gharabaghi, B. (2022). Extreme Learning Machine model for assessment of stream health using the Qualitative Habitat Evaluation Index. Water Supply, 22(5), 5355-5375. https://doi.org/10.2166/ws.2022.166
• MacKenzie, K. M., Gharabaghi, B., Binns, A. D., & Whiteley, H. R. (2022). Early detection model for the urban stream syndrome using specific stream power and regime theory. Journal of Hydrology, 604, 127167. https://doi.org/10.1016/j.jhydrol.2021.127167
• Nguyen, D., Binns, A. D., & Gharabaghi, B. (2020). Physical Laboratory Modeling of the Sediment Transport and Planform Morphological Adjustments in Skewed Meandering Rivers. In Geological Society of America Abstracts (Vol. 52, p. 356104). 10.1130/abs/2020AM-356104
• Milukow, H. A., Binns, A. D., Adamowski, J., Bonakdari, H., & Gharabaghi, B. (2019). Estimation of the Darcy–Weisbach friction factor for ungauged streams using Gene Expression Programming and Extreme Learning Machines. Journal of Hydrology, 568, 311-321. https://doi.org/10.1016/j.jhydrol.2018.10.073
• Gharabaghi, B., & Sattar, A. M. (2017). Empirical models for longitudinal dispersion coefficient in natural streams. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2017.01.022
• Ou, X., Gharabaghi, B., McBean, E., & Doherty, C. (2017). Investigation of the Tank Model for Urban Storm Water Management. Journal of Water Management Modeling. https://doi.org/10.14796/JWMM.C421
• Atieh, M., Taylor, G., Sattar, A. M., & Gharabaghi, B. (2017). Prediction of flow duration curves for ungauged basins. Journal of Hydrology, 545, 383-394. https://doi.org/10.1016/j.jhydrol.2016.12.048
• Sattar, A. M., Plesiński, K., Radecki-Pawlik, A., & Gharabaghi, B. (2017). Scour depth model for grade-control structures. Journal of Hydroinformatics, 20(1), 117-133. https://doi.org/10.2166/hydro.2017.149  
• Gazendam, E., Gharabaghi, B., Ackerman, J. D., & Whiteley, H. (2016). Integrative neural networks models for stream assessment in restoration projects. Journal of Hydrology, 536, 339-350. https://doi.org/10.1016/j.jhydrol.2016.02.057
• Atieh, M., Mehltretter, S. L., Gharabaghi, B., & Rudra, R. (2015). Integrative neural networks model for prediction of sediment rating curve parameters for ungauged basins. Journal of Hydrology, 531, 1095-1107. https://doi.org/10.1016/j.jhydrol.2015.11.008
• Atieh, M., Gharabaghi, B., & Rudra, R. (2015). Entropy-based neural networks model for flow duration curves at ungauged sites. Journal of Hydrology, 529, 1007-1020. https://doi.org/10.1016/j.jhydrol.2015.08.068
• Sattar, A. M., & Gharabaghi, B. (2015). Gene expression models for prediction of longitudinal dispersion coefficient in streams. Journal of Hydrology, 524, 587-596. https://doi.org/10.1016/j.jhydrol.2015.03.016
• Disley, T., Gharabaghi, B., Mahboubi, A. A., & McBean, E. A. (2015). Predictive equation for longitudinal dispersion coefficient. Hydrological processes, 29(2), 161-172. https://doi.org/10.1002/hyp.10139
• Gazendam, E., Gharabaghi, B., Jones, F. C., & Whiteley, H. (2011). Evaluation of the Qualitative Habitat Evaluation Index as a planning and design tool for restoration of rural Ontario waterways. Canadian Water Resources Journal, 36(2), 149-158. https://doi.org/10.4296/cwrj3602827
• Inkratas, C., Gharabaghi, B., Beltaos, S., & Krishnappan, B. (2009). 3D modelling of ice-covered flows in the vicinity of a deep hole in the East Channel of the Mackenzie Delta, NWT. Canadian Journal of Civil Engineering, 36(5), 791-800. https://doi.org/10.1139/L09-031
• Gazendam, E., Gharabaghi, B., McBean, E., Whiteley, H., & Kostaschuk, R. (2009). Ranking of waterways susceptible to adverse stormwater effects. Canadian Water Resources Journal, 34(3), 205-228. https://doi.org/10.4296/cwrj3403205
• Gharabaghi, B., Inkratas, C., Beltaos, S., & Krishnappan, B. (2007). Modelling of three-dimensional flow velocities in a deep hole in the East Channel of the Mackenzie Delta, Northwest Territories. Canadian Journal of Civil Engineering, 34(10), 1312-1323. https://doi.org/10.1139/l07-054
• Gharabaghi, B., Inkratas, C., Krishnappan, B. G., & Rudra, R. P. (2007). Flow characteristics in a rotating circular flume. Open Civil Engineering Journal, 1(1), 30-36. https://doi.org/10.2174/1874149500701010030

Hydrologic Time Series & Flood Forecasting

• Kaur, B., Binns, A., McBean, E., Sandink, D., Castro, K., & Gharabaghi, B. (2025). Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models. Journal of Flood Risk Management, 18(2), e70051. https://doi.org/10.1111/jfr3.70051
• Amiri, A., Gumiere, S. J., Gharabaghi, B., & Bonakdari, H. (2025). From warm seas to flooded streets: The impact of sea surface temperature on cutoff low and extreme rainfall in Valencia, Spain. Journal of Flood Risk Management, 18(1). https://doi.org/10.1111/jfr3.13055
• Costa Rocha, P. A., Oliveira Santos, V., Scott, J., Van Griensven Thé, J., & Gharabaghi, B. (2024). Application of graph neural networks to forecast urban flood events: the case study of the 2013 flood of the Bow River, Calgary, Canada. International Journal of River Basin Management, 1-18. https://doi.org/10.1080/15715124.2024.2329243
• Zhang, Y., Zhou, Z., Deng, Y., Pan, D., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2024). Daily streamflow forecasting using networks of real-time monitoring stations and hybrid machine learning methods. Water, 16(9), 1284. https://doi.org/10.3390/w16091284
• Ebtehaj, I., Bonakdari, H., Gharabaghi, B., & Khelifi, M. (2023). Short-Term precipitation forecasting based on the improved extreme learning machine technique. Environmental Sciences Proceedings, 25(1), 50. https://doi.org/10.3390/ECWS-7-14237
• Jiang, A., McBean, E., Zeng, P., Wang, Y., Chen, H., Binns, A., & Gharabaghi, B. (2023). Understanding changes in the timing of heavy storms: a regional case study of climate change impacts. Climatic Change, 176(12), 172. https://doi.org/10.1007/s10584-023-03644-8
• Zhang, Y., Zhou, Z., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2023). Flood forecasting using hybrid LSTM and GRU models with lag Time Preprocessing. Water, 15(22), 3982. https://doi.org/10.3390/w15223982
• Grambow, K. (2023). Investigating River Ice Thickness and Analyzing Ice Breakup Flooding Events for Canadian Rivers (Doctoral dissertation, University of Guelph). https://hdl.handle.net/10214/27864
• Zhang, Y., Pan, D., Van Griensven, J., Yang, S. X., & Gharabaghi, B. (2023). Intelligent flood forecasting and warning: A survey. Intelligence & Robotics, 3(2), 190-212. https://doi.org/10.20517/ir.2023.12
• Oliveira Santos, V., Costa Rocha, P. A., Scott, J., Thé, J. V. G., & Gharabaghi, B. (2023). A new graph-based deep learning model to predict flooding with validation on a case study on the humber river. Water, 15(10), 1827. https://doi.org/10.3390/w15101827
• Ahmed, S. I., Rudra, R., Goel, P., Khan, A., Gharabaghi, B., & Sharma, R. (2022). A comparative evaluation of using rain gauge and NEXRAD radar-estimated rainfall data for simulating streamflow. Hydrology, 9(8), 133. https://doi.org/10.3390/hydrology9080133
• Zhang, Y., Gu, Z., Thé, J. V. G., Yang, S. X., & Gharabaghi, B. (2022). The discharge forecasting of multiple monitoring station for humber river by hybrid LSTM models. Water, 14(11), 1794. https://doi.org/10.3390/w14111794
• Langridge, M., Gharabaghi, B., Bonakdari, H., & Walton, R. (2022). Understanding the Dynamic Nature of Catchment Response Time through Machine Learning Analysis. Authorea Preprints. https://doi.org/10.1002/essoar.10501417.1
• Elkurdy, M., Binns, A. D., Bonakdari, H., Gharabaghi, B., & McBean, E. (2022). Early detection of riverine flooding events using the group method of data handling for the Bow River, Alberta, Canada. International Journal of River Basin Management, 20(4), 533-544. https://doi.org/10.1080/15715124.2021.1906261
• Taraky, Y. M., Liu, Y., Gharabaghi, B., McBean, E., Daggupati, P., & Shrestha, N. K. (2022). Influence of headwater reservoirs on climate change impacts and flood frequency in the Kabul River Basin. Canadian Journal of Civil Eng., 49(7), 1300-1309. https://doi.org/10.1139/cjce-2020-0840
• Taraky, Y. M., Liu, Y., McBean, E., Daggupati, P., & Gharabaghi, B. (2021). Flood risk management with transboundary conflict and cooperation dynamics in the Kabul River Basin. Water, 13(11), 1513. https://doi.org/10.3390/w13111513
• Soltani, K., Azari, A., Zeynoddin, M., Amiri, A., Ebtehaj, I., Ouarda, T. B.,  & Bonakdari, H. (2021). Lake surface area forecasting using integrated satellite-SARIMA-long-short-term memory model. https://doi.org/10.21203/rs.3.rs-631247/v1
• Azari, A., Zeynoddin, M., Ebtehaj, I., Sattar, A. M., Gharabaghi, B., & Bonakdari, H. (2021). Integrated preprocessing techniques with linear stochastic approaches in groundwater level forecasting. Acta Geophysica, 69(4), 1395-1411. https://doi.org/10.1007/s11600-021-00617-2
• Alizamir, M., Kim, S., Zounemat-Kermani, M., Heddam, S., Shahrabadi, A. H., & Gharabaghi, B. (2021). Modelling daily soil temperature by hydro-meteorological data at different depths using a novel data-intelligence model: deep echo state network model. Artificial Intelligence Review, 54, 2863-2890. https://doi.org/10.1007/s10462-020-09915-5
• Riahi-Madvar, H., Dehghani, M., Memarzadeh, R., & Gharabaghi, B. (2021). Short to long-term forecasting of river flows by heuristic optimization algorithms hybridized with ANFIS. Water Resources Management, 35, 1149-1166. https://doi.org/10.1007/s11269-020-02756-5
• Lotfi, K., Bonakdari, H., Ebtehaj, I., Rezaie-Balf, M., Samui, P., Sattar, A. A., & Gharabaghi, B. (2021). River flow forecasting using stochastic and neuro-fuzzy-embedded technique: a comprehensive preprocessing-based assessment. In Water Engineering Modeling and Mathematic Tools (pp. 519-549). Elsevier. https://doi.org/10.1016/B978-0-12-820644-7.00010-4
• Soltani, K., Amiri, A., Zeynoddin, M., Ebtehaj, I., Gharabaghi, B., & Bonakdari, H. (2021). Forecasting monthly fluctuations of lake surface areas using remote sensing techniques and novel machine learning methods. Theoretical and Applied Climatology, 143, 713-735. https://doi.org/10.1007/s00704-020-03419-6
• Soltani, K., Ebtehaj, I., Amiri, A., Azari, A., Gharabaghi, B., & Bonakdari, H. (2021). Mapping the spatial and temporal variability of flood susceptibility using remotely sensed normalized difference vegetation index and the forecasted changes in the future. Science of The Total Environment, 770, 145288. https://doi.org/10.1016/j.scitotenv.2021.145288
• Langridge, M., McBean, E., Bonakdari, H., & Gharabaghi, B. (2021). A dynamic prediction model for time‐to‐peak. Hydrological Processes, 35(1), e14032. https://doi.org/10.1002/hyp.14032 
• Langridge, M., Gharabaghi, B., McBean, E., Bonakdari, H., & Walton, R. (2020). Understanding the dynamic nature of Time-to-Peak in UK streams. Journal of Hydrology, 583, 124630. https://doi.org/10.1016/j.jhydrol.2020.124630
• Bonakdari, H., Binns, A. D., & Gharabaghi, B. (2020). A comparative study of linear stochastic with nonlinear daily river discharge forecast models. Water Resources Management, 34(11), 3689-3708. https://doi.org/10.1007/s11269-020-02644-y
• Zeynoddin, M., Bonakdari, H., Ebtehaj, I., Azari, A., & Gharabaghi, B. (2020). A generalized linear stochastic model for lake level prediction. Science of The Total Environment, 723, 138015. https://doi.org/10.1016/j.scitotenv.2020.138015
• Ebtehaj, I., Bonakdari, H., Zeynoddin, M., Gharabaghi, B., & Azari, A. (2020). Evaluation of preprocessing techniques for improving the accuracy of stochastic rainfall forecast models. International Journal of Environmental Science and Technology, 17, 505-524. https://doi.org/10.1007/s13762-019-02361-z
• Elkurdy, M., Binns, A. D., & Gharabaghi, B. (2020). Improved Streamflow Forecasting and Flood Warning Using Variational Mode Decomposition and Extreme Gradient Boosting. In Geological Society of America Abstracts (Vol. 52, p. 359484). 10.1130/abs/2020AM-359484 
• Bonakdari, H., Zaji, A. H., Soltani, K., & Gharabaghi, B. (2020). Improving the accuracy of a remotely-sensed flood warning system using a multi-objective pre-processing method for signal defects detection and elimination. Comptes Rendus. Géoscience, 352(1), 73-86. https://doi.org/10.5802/crgeos.4
• Langridge, M., Gharabaghi, B., Bonakdari, H., & Walton, R. (2019). Understanding the Dynamic Nature of Catchment Response Time through Machine Learning Analysis. https://doi.org/10.1002/essoar.10501417.1
• Zaji, A. H., Bonakdari, H., & Gharabaghi, B. (2019). Advancing Freshwater Lake Level Forecast Using King’s Castle Optimization with Training Sample Adaption and Adaptive Neuro-Fuzzy Inference System. Water Resources Management, 33(12), 4215-4230. https://doi.org/10.1007/s11269-019-02356-y
• Ebtehaj, I., Bonakdari, H., & Gharabaghi, B. (2019). A reliable linear method for modeling lake level fluctuations. Journal of Hydrology, 570, 236-250. https://doi.org/10.1016/j.jhydrol.2019.01.010
• Zeynoddin, M., Bonakdari, H., Ebtehaj, I., Esmaeilbeiki, F., Gharabaghi, B., & Haghi, D. Z. (2019). A reliable linear stochastic daily soil temperature forecast model. Soil and Tillage Research, 189, 73-87. https://doi.org/10.1016/j.still.2018.12.023
• Bonakdari, H., Moeeni, H., Ebtehaj, I., Zeynoddin, M., Mahoammadian, A., & Gharabaghi, B. (2019). New insights into soil temperature time series modeling: linear or nonlinear?. Theoretical and Applied Climatology, 135(3-4), 1157-1177. https://doi.org/10.1007/s00704-018-2436-2
• Jiang, A. Z., McBean, E. A., Binns, A., & Gharabaghi, B. (2019). Quantifying Rainfall-Derived Inflow from Private Foundation Drains in Sanitary Sewers: Case Study in London, Ontario, Canada. Journal of Hydrologic Engineering, 24(9), 05019023. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001814
• Walton, R., Binns, A., Bonakdari, H., Ebtehaj, I., & Gharabaghi, B. (2019). Estimating 2-year flood flows using the generalized structure of the Group Method of Data Handling. Journal of Hydrology, 575, 671-689. https://doi.org/10.1016/j.jhydrol.2019.05.068
• Bonakdari, H., Zaji, A. H., Binns, A. D., & Gharabaghi, B. (2019). Integrated Markov chains and uncertainty analysis techniques to more accurately forecast floods using satellite signals. Journal of Hydrology, 572, 75-95. https://doi.org/10.1016/j.jhydrol.2019.02.027
• Sattar, A., Bonakdari, H., Gharabaghi, B., & Radecki-Pawlik, A. (2019). Hydraulic Modeling and Evaluation Equations for the Incipient Motion of Sandbags for Levee Breach Closure Operations. Water, 11(2), 279. https://doi.org/10.3390/w11020279
• Zaji, A. H., Bonakdari, H., & Gharabaghi, B. (2018). Applying upstream satellite signals and a 2-D error minimization algorithm to advance early warning and management of flood water levels and river discharge. IEEE Transactions on Geoscience and Remote Sensing, 57(2), 902-910. https://doi.org/10.1109/tgrs.2018.2862640
• Zaji, A. H., Bonakdari, H., & Gharabaghi, B. (2018). Remote sensing satellite data preparation for simulating and forecasting river discharge. IEEE Transactions on Geoscience and Remote Sensing, 56(6), 3432-3441. https://doi.org/10.1109/tgrs.2018.2799901
• Vrban, S., Wang, Y., McBean, E. A., Binns, A., & Gharabaghi, B. (2018). Evaluation of Stormwater Infrastructure Design Storms Developed Using Partial Duration and Annual Maximum Series Models. Journal of Hydrologic Engineering, 23(12), 04018051. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001712
• Perdikaris, J., Gharabaghi, B., & Rudra, R. (2018). Evaluation of the simplified dynamic wave, diffusion wave and the full dynamic wave flood routing models. Earth Sci. Res, 7(2), 14. https://doi.org/10.5539/esr.v7n2p14
• Perdikaris, J., Gharabaghi, B., & Rudra, R. (2018). Reference time of concentration estimation for ungauged catchments. Earth Sci. Res, 7, 58-73. https://doi.org/10.5539/esr.v7n2p58
• Zeynoddin, M., Bonakdari, H., Azari, A., Ebtehaj, I., Gharabaghi, B., & Madavar, H. R. (2018). Novel hybrid linear stochastic with non-linear extreme learning machine methods for forecasting monthly rainfall a tropical climate. Journal of Environmental Management, 222, 190-206. https://doi.org/10.1016/j.jenvman.2018.05.072
• Zaji, A. H., Bonakdari, H., & Gharabaghi, B. (2018). Reservoir water level forecasting using group method of data handling. Acta Geophysica, 66(4), 717-730. https://doi.org/10.1007/s11600-018-0168-4
• Kozyn, A., Songin, K., Gharabaghi, B., & Lubitz, W. D. (2018). Predicting Archimedes Screw Generator Power Output Using Artificial Neural Networks. Journal of Hydraulic Engineering, 144(3), 05018002. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001433
• Perdikaris, J., Gharabaghi, B., & McBean, E. (2011). A methodology for undertaking vulnerability assessments of flood susceptible communities. International journal of safety and security engineering, 1(2), 126-146. https://doi.org/10.2495/SAFE-V1-N2-126-146

Urban Stormwater Management

• Stajkowski, S., Laleva, A., Farghaly, H., Bonakdari, H., & Gharabaghi, B. (2021). Modelling dry-weather temperature profiles in urban stormwater management ponds. Journal of Hydrology, 598, 126206. https://doi.org/10.1016/j.jhydrol.2021.126206
• Trenouth, W. R., Gharabaghi, B., & Farghaly, H. (2018). Enhanced roadside drainage system for environmentally sensitive areas. Science of The Total Environment, 610, 613-622. https://doi.org/10.1016/j.scitotenv.2017.08.081
• Ebtehaj, I., Bonakdari, H., & Gharabaghi, B. (2018). Development of more accurate discharge coefficient prediction equations for rectangular side weirs using adaptive neuro-fuzzy inference system and generalized group method of data handling. Measurement, 116, 473-482. https://doi.org/10.1016/j.measurement.2017.11.023
• Azimi, H., Bonakdari, H., Ebtehaj, I., Gharabaghi, B., & Khoshbin, F. (2018). Evolutionary design of generalized group method of data handling-type neural network for estimating the hydraulic jump roller length. Acta Mechanica, 229(3), 1197-1214. https://doi.org/10.1007/s00707-017-2043-9
• Sattar, A. M. A., Gharabaghi, B., Sabouri, F., & Thompson, A. M. (2017). Urban stormwater thermal gene expression models for protection of sensitive receiving streams. Hydrological processes, 31(13), 2330-2348. https://doi.org/10.1002/hyp.11170
• Sattar, A. M. A., & Gharabaghi, B. (2017). Gene Expression Programming in Open Channel Hydraulics. In Open Channel Hydraulics, River Hydraulic Structures and Fluvial Geomorphology (pp. 196-211). CRC Press. https://doi.org/10.1201/9781315120584-11
• Trenouth, W. R., & Gharabaghi, B. (2016). Highway runoff quality models for the protection of environmentally sensitive areas. Journal of Hydrology, 542, 143-155. https://doi.org/10.1016/j.jhydrol.2016.08.058
• Sabouri, F., Gharabaghi, B., Sattar, A. M. A., & Thompson, A. M. (2016). Event-based stormwater management pond runoff temperature model. Journal of Hydrology, 540, 306-316. https://doi.org/10.1016/j.jhydrol.2016.06.017
• Sabouri, F., Gharabaghi, B., McBean, E., & Tu, C. (2016). Thermal investigation of stormwater management ponds. Journal of Water Management Modeling. https://doi.org/10.14796/JWMM.C397
• Trenouth, W. R., & Gharabaghi, B. (2015). Soil amendments for heavy metals removal from stormwater runoff discharging to environmentally sensitive areas. Journal of Hydrology, 529, 1478-1487. https://doi.org/10.1016/j.jhydrol.2015.08.034
• Sabouri, F., Gharabaghi, B., Mahboubi, A. A., & McBean, E. A. (2013). Impervious surfaces and sewer pipe effects on stormwater runoff temperature. Journal of Hydrology, 502, 10-17. https://doi.org/10.1016/j.jhydrol.2013.08.016
• Sabouri, F., Gharabaghi, B., Perera, N., & McBean, E. (2013). Evaluation of the thermal impact of stormwater management ponds. CHI Journal of Water Management Modeling. https://doi.org/10.14796/JWMM.R246-12
• Finney, K., & Gharabaghi, B. (2011). Using the PCSWMM 2010 SRTC tool to design a compost biofilter for highway stormwater runoff treatment. Journal of Water Management Modeling. https://www.doi.org/10.14796/JWMM.R241-09
• Finney, K., Gharabaghi, B., McBean, E., Rudra, R., & MacMillan, G. (2010). Compost biofilters for highway stormwater runoff treatment. Water Quality Research Journal, 45(4), 391-402.
• Ahmed, S. I., Rudra, R. P., Gharabaghi, B., & Pedikaris, J. (2007). Change in IDF curves for a River basin in southern Ontario. In 2007 ASAE Annual Meeting (p. 1). American Society of Agricultural and Biological Engineers. https://doi.org/10.13031/2013.23207
• Bradford, A., & Gharabaghi, B. (2004). Evolution of Ontario’s stormwater management planning and design guidance. Water Quality Research Journal, 39(4), 343-355. https://doi.org/10.2166/wqrj.2004.047

Identification & Protection of Salt Vulnerable Areas

• Tabrizi, S. E., Elizarov, J., Farghaly, H., & Gharabaghi, B. (2025). Precision salt application using advanced machine learning algorithms to achieve improved road safety and reduced environmental impacts. Journal of Traffic and Transportation Engineering. https://doi.org/10.1016/j.jtte.2024.11.004
• Radosavljevic, J., Slowinski, S., Rezanezhad, F., Shafii, M., Gharabaghi, B., & Van Cappellen, P. (2024). Road salt-induced salinization impacts water geochemistry and mixing regime of a Canadian urban lake. Applied Geochemistry, 162, 105928. https://doi.org/10.1016/j.apgeochem.2024.105928
• Tabrizi, S. E., Hippi, M., Sullivan, J., Farghaly, H., & Gharabaghi, B. (2024). Real-Time Monitoring and Forecasting Ice Layer Thickness Growth Rate and Grip Loss on a Road Network During Winter Storm Events. Transportation Research Record, 1, 12. https://doi.org/10.1177/03611981241275580
• Santos, V. O., Rocha, P. A. C., & Gharabaghi, B. (2023). Graph-based deep learning model for forecasting chloride concentration in urban streams to protect salt-vulnerable areas. Environments, 10(9), 157. http://dx.doi.org/10.20944/preprints202308.0450.v1
• Tabrizi, S. E., Pringle, J., Moosavi, Z., Amouzadeh, A., Farghaly, H., Trenouth, W. R., & Gharabaghi, B. (2022). Protecting Salt Vulnerable Areas Using an Enhanced Roadside Drainage System (ERDS). Water, 14(22), 3773. https://doi.org/10.3390/w14223773
• Tabrizi, S. E., Xiao, K., Thé, J. V. G., Saad, M., Farghaly, H., Yang, S. X., & Gharabaghi, B. (2021). Hourly road pavement surface temperature forecasting using deep learning models. Journal of Hydrology, 603, 126877. https://doi.org/10.1016/j.jhydrol.2021.126877
• McTaggart, D., Trenouth, W. R., Stajowski, S., Farghaly, H., & Gharabaghi, B. (2018). Compost Biofilters for Protection of Environmentally Sensitive Areas Receiving Roadway Runoff. Earth Science Research, 7(2), 88. https://doi.org/10.5539/esr.v7n2p88  
• Delbari, M., Afrasiab, P., Gharabaghi, B., Amiri, M., & Salehian, A. (2019). Spatial variability analysis and mapping of soil physical and chemical attributes in a salt-affected soil. A. Journal of Geosciences, 12(3), 68. https://doi.org/10.1007/s12517-018-4207-x
• Salek, M., Levison, J., Parker, B., & Gharabaghi, B. (2018). CAD-DRASTIC: chloride application density combined with DRASTIC for assessing groundwater vulnerability to road salt application. Hydrogeology Journal, 26(7), 2379-2393. https://doi.org/10.1007/s10040-018-1801-7
• Trenouth, W. R., Gharabaghi, B., & Perera, N. (2015). Road salt application planning tool for winter de-icing operations. Journal of Hydrology, 524, 401-410. https://doi.org/10.1016/j.jhydrol.2015.03.004
• Betts, A., Gharabaghi, B., McBean, E., Levison, J., & Parker, B. (2015). Salt vulnerability assessment methodology for municipal supply wells. Journal of Hydrology, 531, 523-533. https://doi.org/10.1016/j.jhydrol.2015.11.004
• Betts, A. R., Gharabaghi, B., & McBean, E. A. (2014). Salt vulnerability assessment methodology for urban streams. Journal of Hydrology, 517, 877-888. https://doi.org/10.1016/j.jhydrol.2014.06.005
• Kilgour, B. W., Gharabaghi, B., & Perera, N. (2013). Ecological benefit of the road salt code of practice. Water quality research journal of Canada, 49(1), 43-52. https://doi.org/10.2166/wqrjc.2013.129
• Perera, N., Gharabaghi, B., & Howard, K. (2013). Groundwater chloride response in the Highland Creek watershed due to road salt application: A re-assessment after 20 years. Journal of Hydrology, 479, 159-168. https://doi.org/10.1016/j.jhydrol.2012.11.057
• Perera, N., Gharabaghi, B., Noehammer, P., & Kilgour, B. (2010). Road salt application in Highland Creek watershed, Toronto, Ontario-chloride mass balance. Water Quality Research Journal, 45(4), 451-461. https://doi.org/10.2166/wqrj.2010.044
• Perera, N., Gharabaghi, B., & Noehammer, P. (2009). Stream chloride monitoring program of City of Toronto: implications of road salt application. Water Quality Research Journal, 44(2), 132-140. https://doi.org/10.2166/wqrj.2009.014

Urban Water Infrastructure Management

• Sattar, A. M., Ghazal, A. N., Elhakeem, M., Elansary, A. S., & Gharabaghi, B. (2023). Application of Machine Learning Coupled with Stochastic Numerical Analyses for Sizing Hybrid Surge Vessels on Low-Head Pumping Mains. Water, 15(19), 3525. https://doi.org/10.3390/w15193525
• Siwakoti, S., Binns, A., Bradford, A., Bonakdari, H., & Gharabaghi, B. (2023). A Prediction Model to Cost-Optimize Clean-Out of Permeable Interlocking Concrete Pavers. Water, 15(11), 2135. https://doi.org/10.3390/w15112135
• Stajkowski, S., Hotson, E., Zorica, M., Farghaly, H., Bonakdari, H., McBean, E., & Gharabaghi, B. (2023). Modeling stormwater management pond thermal impacts during storm events. Journal of Hydrology, 620, 129413. https://doi.org/10.1016/j.jhydrol.2023.129413
• Jiang, A. Z., McBean, E. A., Binns, A. D., & Gharabaghi, B. (2022). Guidance on field survey programme design for basement flooding assessment. Hydrological Sciences Journal, 67(16), 2524-2533. https://doi.org/10.1080/02626667.2020.1782412
• Noori, A., Bonakdari, H., Hassaninia, M., Morovati, K., Khorshidi, I., Noori, A., & Gharabaghi, B. (2022). A reliable GIS-based FAHP-FTOPSIS model to prioritize urban water supply management scenarios: A case study in semi-arid climate. Sustainable Cities and Society, 81, 103846. https://doi.org/10.1016/j.scs.2022.103846
• Clement, D., Aliabadi, A. A., Mackey, J., Thé, J., & Gharabaghi, B. (2021). Dust emissions management model for construction sites. Journal of Construction Engineering and Management, 147(8), 04021092. https://doi.org/10.1061/(ASCE)CO.1943-7862.0002121
• Noori, A., Bonakdari, H., Salimi, A. H., & Gharabaghi, B. (2021). A group Multi-Criteria Decision-Making method for water supply choice optimization. Socio-Economic Planning Sciences, 77, 101006. https://doi.org/10.1016/j.seps.2020.101006
• Dusolt, S., Binns, A., McBean, E., Gharabaghi, B., & Sandink, D. (2021). Characterization of backwater valves in sanitary sewer laterals and associated failures in a Canadian context. Canadian Journal of Civil Engineering, 48(7), 829-837. https://doi.org/10.1139/cjce-2020-0026
• Stajkowski, S., Gharabaghi, B., & Farghaly, H. (2020, December). Forecasting Stormwater Pond Dry-Weather Water Temperature Profiles Using the Group Method of Data Handling. In AGU Fall Meeting Abstracts (Vol. 2020, pp. H166-0015). https://doi.org/10.1016/j.jhydrol.2021.126206
• Lotfi, K., Bonakdari, H., Ebtehaj, I., Delatolla, R., Zinatizadeh, A. A., & Gharabaghi, B. (2020). A novel stochastic wastewater quality modeling based on fuzzy techniques. Journal of Environmental Health Science and Engineering, 18, 1099-1120. http://dx.doi.org/10.1007/s40201-020-00530-8
• Noori, A., Bonakdari, H., Morovati, K., & Gharabaghi, B. (2020). Development of optimal water supply plan using integrated fuzzy Delphi and fuzzy ELECTRE III methods—Case study of the Gamasiab basin. Expert Systems, 37(5), e12568. https://doi.org/10.1111/exsy.12568
• Stajkowski, S., Zeynoddin, M., Farghaly, H., Gharabaghi, B., & Bonakdari, H. (2020). A methodology for forecasting dissolved oxygen in urban streams. Water, 12(9), 2568. https://ui.adsabs.harvard.edu/abs/2020AGUFMH166.0015S/abstract
• Stajkowski, S., Kumar, D., Samui, P., Bonakdari, H., & Gharabaghi, B. (2020). Genetic-algorithm-optimized sequential model for water temperature prediction. Sustainability, 12(13), 5374. https://doi.org/10.3390/su12135374
• Ebtehaj, I., Bonakdari, H., Safari, M. J. S., Gharabaghi, B., Zaji, A. H., Madavar, H. R.,  & Mehr, A. D. (2020). Combination of sensitivity and uncertainty analyses for sediment transport modeling in sewer pipes. International Journal of Sediment Research, 35(2), 157-170. https://doi.org/10.1016/j.ijsrc.2019.08.005
• Salimi, A. H., Noori, A., Bonakdari, H., Masoompour Samakosh, J., Sharifi, E., Hassanvand, M., & Agharazi, M. (2020). Exploring the role of advertising types on improving the water consumption behavior: An application of integrated fuzzy AHP and fuzzy VIKOR method. Sustainability, 12(3), 1232. https://doi.org/10.3390/su12031232
• Binns, A. D., Fata, A., Ferreira da Silva, A. M., Bonakdari, H., & Gharabaghi, B. (2019). Modeling Performance of Sediment Control Wet Ponds at Two Construction Sites in Ontario, Canada. Journal of Hydraulic Engineering, 145(4), 05019001. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001581
• Lotfi, K., Bonakdari, H., Ebtehaj, I., Mjalli, F. S., Zeynoddin, M., Delatolla, R., & Gharabaghi, B. (2019). Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology. Journal of Environmental Management, 240, 463-474. https://doi.org/10.1016/j.jenvman.2019.03.137
• Sattar, A. A., Elhakeem, M., Rezaie-Balf, M., Gharabaghi, B., & Bonakdari, H. (2019). Artificial intelligence models for prediction of the aeration efficiency of the stepped weir. Flow Measurement and Instrumentation, 65, 78-89. https://doi.org/10.1016/j.flowmeasinst.2018.11.017
• Bonakdari, H., Ebtehaj, I., Gharabaghi, B., Vafaeifard, M., & Akhbari, A. (2019). Calculating the energy consumption of electrocoagulation using a generalized structure group method of data handling integrated with a genetic algorithm and singular value decomposition. Clean Technologies and Environmental Policy, 21(2), 379-393. https://doi.org/10.1007/s10098-018-1642-z
• Akhbari, A., Ibrahim, S., Zinatizadeh, A. A., Bonakdari, H., Ebtehaj, I., S. Khozani, Z., & Gharabaghi, B. (2019). Evolutionary prediction of biohydrogen production by dark fermentation. CLEAN–Soil, Air, Water, 47(1), 1700494. https://doi.org/10.1002/clen.201700494
• Sattar, A. M., Ertuğrul, Ö. F., Gharabaghi, B., McBean, E. A., & Cao, J. (2019). Extreme learning machine model for water network management. Neural Computing and Applications, 31(1), 157-169. https://doi.org/10.1007/s00521-017-2987-7
• Bonakdari, H., Zaji, A. H., Gharabaghi, B., Ebtehaj, I., & Moazamnia, M. (2018). More accurate prediction of the complex velocity field in sewers based on uncertainty analysis using extreme learning machine technique. ISH Journal of Hydraulic Engineering, 1-12. https://doi.org/10.1080/09715010.2018.1498753
• Bostan, M., Akhtari, A. A., Bonakdari, H., Gharabaghi, B., & Noori, O. (2018). Investigation of a new shock damper system efficiency in reducing water hammer excess pressure due to the sudden closure of a control valve. ISH Journal of Hydraulic Engineering, 1-9. https://doi.org/10.1080/09715010.2018.1479665
• Post, Y. L., McBean, E., & Gharabaghi, B. (2018). Using Probabilistic Neural Networks to Analyze First Nations’ Drinking Water Advisory Data. Journal of Water Resources Planning and Management, 144(11), 05018015. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000988
• Sattar, A. M. A., Bonakdari, H., Negm, A., Gharabaghi, B., & Elhakeem, M. (2018). Soil Aquifer Treatment System Design Equation for Organic Micropollutant Removal. In Groundwater in the Nile Delta (pp. 307-326). Springer, Cham. https://doi.org/10.1007/698_2017_136
• Uduma, U. A., McBean, E. A., & Gharabaghi, B. (2017). Risk assessment of cyanobacteria-toxins for small drinking water treatment plants with lake water intakes. International Journal of Water Resources and Environmental Engineering, 9(6), 121-126. https://doi.org/10.5897/ijwree2016.0669
• Abbassi, B.E., Abu Saleem, M., Zytner, R.G., Gharabaghi, B., & Rudra R. (2016). Antibiotics in wastewater: Their degradation and effect on wastewater treatment efficiency. Food, Agriculture and Environment (JFAE), Vol. 14, Issue 3&4, pages 95-99. Online ISSN: 1459-0263.
• Sattar, A. M., Gharabaghi, B., & McBean, E. A. (2016). Prediction of timing of watermain failure using gene expression models. Water resources management, 30(5), 1635-1651. https://doi.org/10.1007/s11269-016-1241-x
• Thompson, J., Sattar, A. M., Gharabaghi, B., & Warner, R. C. (2016). Event-based total suspended sediment particle size distribution model. Journal of Hydrology, 536, 236-246. https://doi.org/10.1016/j.jhydrol.2016.02.056
• Trenouth, W. R., & Gharabaghi, B. (2015). Event-based design tool for construction site erosion and sediment controls. Journal of Hydrology, 528, 790-795. https://doi.org/10.1016/j.jhydrol.2015.06.054
• Trenouth, W. R., & Gharabaghi, B. (2015). Event-based soil loss models for construction sites. Journal of Hydrology, 524, 780-788. https://doi.org/10.1016/j.jhydrol.2015.03.010
• Harvey, R., Murphy, H. M., McBean, E. A., & Gharabaghi, B. (2015). Using data mining to understand drinking water advisories in small water systems: A case study of Ontario First Nations drinking water supplies. Water resources management, 29(14), 5129-5139. https://doi.org/10.1007/s11269-015-1108-6
• Harvey, R., McBean, E. A., & Gharabaghi, B. (2013). Predicting the timing of water main failure using artificial neural networks. Journal of Water Resources Planning and Management, 140(4), 425-434. https://doi.org/10.1061/(ASCE)WR.1943-5452.0000354
• Asnaashari, A., McBean, E. A., Gharabaghi, B., & Tutt, D. (2013). Forecasting watermain failure using artificial neural network modelling. Canadian Water Resources Journal, 38(1), 24-33. https://doi.org/10.1080/07011784.2013.774153
• Trenouth, W. R., Gharabaghi, B., MacMillan, G., & Bradford, A. (2013). Better management of construction sites to protect inland waters. Inland Waters, 3(2), 167-178. https://doi.org/10.5268/IW-3.2.515
• Asnaashari, A., McBean, E., Gharabaghi, B., Pourrajab, R., & Shahrour, I. (2010). Survival rate analyses of watermains: a comparison of case studies for Canada and Iran. Journal of Water Management Modeling. https://doi.org/10.14796/JWMM.R236-30 
• Asnaashari, A., McBean, E. A., Shahrour, I., & Gharabaghi, B. (2009). Prediction of watermain failure frequencies using multiple and Poisson regression. Water Science and Technology: Water Supply, 9(1), 9-19. https://doi.org/10.2166/ws.2009.020
• Taleban, V., Finney, K., Gharabaghi, B., McBean, E., Rudra, R., & Van Seters, T. (2009). Effectiveness of compost biofilters in removal of sediments from construction site runoff. Water Quality Research Journal, 44(1), 71-80. https://doi.org/10.2166/wqrj.2009.008
• Gharabaghi, B., Fata, A., Seters, T. V., Rudra, R. P., MacMillan, G., Smith, D., & Tesa, G. (2006). Evaluation of sediment control pond performance at construction sites in the Greater Toronto Area. Canadian Journal of Civil Engineering, 33(11), 1335-1344. https://doi.org/10.1139/l06-074

Agricultural Land and Water Management

• Pereira, R., Gharabaghi, B., Bonakdari, H., & Safadoust, A. (2025). An empirical equation for sediment transport capacity of overland flow: Integrating slope, discharge, and particle size. Soil Science Society of America Journal, 89(1), e270021. https://doi.org/10.1002/saj2.70021
• Rosenfield, A., Ficht, A., Lyons, E. M., & Gharabaghi, B. (2024). A One-Dimensional Light Detection and Ranging Array Scanner for Mapping Turfgrass Quality. Remote Sensing, 16(12), 2215. https://doi.org/10.3390/rs16122215
• Safadoust, A., Dashtpeyma, B., Mosaddeghi, M. R., Asgarzadeh, H., & Gharabaghi, B. (2024). Effect of water salinity and sodicity on soil least limiting water range. Soil Science Society of America Journal, 88(2), 371-386. https://doi.org/10.1002/saj2.20635
• Carey, K., Powers, J. E., Ficht, A., Dance, T., Gharabaghi, B., & Lyons, E. M. (2023). Novel Curve Fitting Analysis of NDVI Data to Describe Turf Fertilizer Response. Agriculture, 13(8), 1532. https://doi.org/10.3390/agriculture13081532
• Ebtehaj, I., Bonakdari, H., Samui, P., & Gharabaghi, B. (2023). Multi-depth daily soil temperature modeling: meteorological variables or time series?. Theoretical and Applied Climatology, 151(3), 989-1012. https://doi.org/10.1007/s00704-022-04314-y
• Seifi, A., Ehteram, M., Nayebloei, F., Soroush, F., Gharabaghi, B., & Torabi Haghighi, A. (2021). GLUE uncertainty analysis of hybrid models for predicting hourly soil temperature and application wavelet coherence analysis for correlation with meteorological variables. Soft computing, 25, 10723-10748. https://doi.org/10.1007/s00500-021-06009-4
• Seifi, A., Ehteram, M., Nayebloei, F., Soroush, F., Gharabaghi, B., & Haghighi, A. T. (2021). Hourly soil temperature prediction using integrated machine learning methods, GLUE uncertainty analysis, Taguchi search, and wavelet coherence analysis. https://doi.org/10.21203/rs.3.rs-285852/v1
• Mundi, G., Zytner, R. G., Warriner, K., Bonakdari, H., & Gharabaghi, B. (2021). Machine learning models for predicting water quality of treated fruit and vegetable wastewater. Water, 13(18), 2485. https://doi.org/10.3390/w13182485
• Jahanfar, A., Drake, J., Gharabaghi, B., & Sleep, B. (2020). An experimental and modeling study of evapotranspiration from integrated green roof photovoltaic systems. 10.1016/j.ecoleng.2020.105767
• Gupta, A. K., Rudra, R. P., Gharabaghi, B., Goel, P. K., Sebti, S., Shukla, R., & Daggupati, P. (2019). A Modeling Approach for Evaluating Watershed-scale Water Quality Benefits of Vegetative Filter Strip-A Case Study in Ontario. Applied Engineering in Agriculture, 35(3), 271-281. https://doi.org/10.13031/aea.13121
• Gupta, A. K., Rudra, R. P., Gharabaghi, B., Daggupati, P., Parkin, G., Goel, P. K., & Shukla, R. (2018) CoBAGNPS: A Toolbox to Estimate Sediment Removal Efficiency of WASCoBs–Pipe Risers and Blind Inlets. Environment and Natural Resources Research, 8(3), 84.https://doi.org/10.5539/enrr.v8n3p84
• Gupta, A. K., Rudra, R. P., Gharabaghi, B., Daggupati, P., Goel, P. K., & Shukla, R. (2018). Predicting the Impact of Drainage Ditches upon Hydrology and Sediment Loads Using KINEROS 2 Model: A Case Study in Ontario. Canadian Biosystems Engineering/Le génie des biosystèmes au Canada, 60, 1-1. https://doi.org/10.7451/CBE.2018.60.1.1
• DeLay, E., & Gharabaghi, B. (2018). A review of low-grade weirs as an agri-environmental best management practice in the Elginfield Municipal Drain watershed, Ontario, Canada. Journal of Soil and Water Conservation, 73(2), 42A-48A. https://doi.org/10.2489/jswc.73.2.42A
• Jahanfar, A., Drake, J., Sleep, B., & Gharabaghi, B. (2018). A modified FAO evapotranspiration model for refined water budget analysis for Green Roof systems. Ecological Engineering, 119, 45-53. https://doi.org/10.1016/j.ecoleng.2018.04.021
• Noori, A., Bonakdari, H., Morovati, K., & Gharabaghi, B. (2018). The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model. Environment Systems and Decisions, 38(4), 471-488. https://doi.org/10.1007/s10669-018-9673-x
• Mundi, G. S., Zytner, R. G., Warriner, K., & Gharabaghi, B. (2018). Predicting fruit and vegetable processing wash-water quality. Water Science and Technology, 2017(1), 256-269.  https://doi.org/10.2166/wst.2018.109
• Atieh, M., Rudra, R., Gharabaghi, B., Golmohammadi, G., Mohammadi, K. (2017). Investigating solutions for irrigation water deficiency in Lebanon. Food, Agriculture and Environment, 15(2), 86-96. https://doi.org/10.1234/4.2017.1122
• Singh, H. V., Thompson, A. M., & Gharabaghi, B. (2016). Event runoff and sediment-yield neural network models for assessment and design of management practices for small agricultural watersheds. Journal of Hydrologic Engineering, 22(2), 04016056. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001457
• Stang, C., Gharabaghi, B., Rudra, R., Golmohammadi, G., Mahboubi, A. A., & Ahmed, S. I. (2016). Conservation management practices: success story of the Hog Creek and Sturgeon River watersheds, Ontario, Canada. Journal of Soil and Water Conservation, 71(3), 237-248. https://doi.org/10.2489/jswc.71.3.237
• Mattar, M. A., Alazba, A. A., Alblewi, B., Gharabaghi, B., & Yassin, M. A. (2016). Evaluating and calibrating reference evapotranspiration models using water balance under hyper-arid environment. Water resources management, 30(11), 3745-3767. https://doi.org/10.1007/s11269-016-1382-y
• Alblewi, B., Gharabaghi, B., Alazba, A. A., & Mahboubi, A. A. (2015). Evapotranspiration models assessment under hyper-arid environment. Arabian Journal of Geosciences, 8(11), 9905-9912. https://doi.org/10.1007/s12517-015-1867-7
• Sayyad, G., Vasel, L., Besalatpour, A. A., Gharabaghi, B., & Golmohammadi, G. (2015). Modeling Blue and Green Water Resources Availability in an Iranian Data Scarce Watershed Using SWAT. Journal of Water Management Modeling. https://doi.org/10.14796/JWMM.C391
• Ahmed, S. I., Singh, A., Rudra, R., & Gharabaghi, B. (2013). Comparison of CANWET and HSPF for water budget and water quality modeling in rural Ontario. Water Quality Research Journal of Canada, 49(1), 53-71. https://doi.org/10.2166/wqrjc.2013.044
• Singh, A., Rudra, R. P., & Gharabaghi, B. (2012). Evaluation of CANWET model for hydrologic simulations for upper Canagagigue Creek watershed in southern Ontario. Canadian Biosystems Engineering, 54(1), 7-18. Part of ISSN: 14929058
• Rudra, R. P., Gharabaghi, B., Sebti, S., Gupta, N., & Moharir, A. (2010). GDVFS: A new toolkit for analysis and design of vegetative filter strips using VFSMOD. Water Quality Research Journal, 45(1), 59-68. https://doi.org/10.2166/wqrj.2010.007
• Das, S., Rudra, R. P., Gharabaghi, B., Gebremeskel, S., Goel, P. K., & Dickinson, W. T. (2008). Applicability of AnnAGNPS for Ontario conditions. Canadian Biosystems Engineering, 50(1), 1-11. EID:2-s2.0-77951848798
• Ricketts, D. D., Rudra, R. P., & Gharabaghi, B. (2007). An irrigation management model for a multi‐cropping and multi‐pattern setting. Irrigation and Drainage: The journal of the International Commission on Irrigation and Drainage, 56(4), 451-462. https://doi.org/10.1002/ird.311
• Motiee, H., Mcbean, E., Semsar, A., Gharabaghi, B., & Ghomashchi, V. (2006). Assessment of the contributions of traditional qanats in sustainable water resources management. International Journal of Water Resources Development, 22(4), 575-588. https://doi.org/10.1080/07900620600551304
• Gharabaghi, B., Rudra, R. P., & Goel, P. K. (2006). Effectiveness of vegetative filter strips in removal of sediments from overland flow. Water Quality Research Journal, 41(3), 275-282. https://doi.org/10.2166/wqrj.2006.031
• Gharabaghi, B., Rudra, R. P., Whiteley, H. R., & Dickinson, W. T. (2002). Development of a management tool for vegetative filter strips. Best Modeling Practices for Urban Water Systems, Monograph, 10, 289. https://doi.org/10.14796/JWMM.R208-18
• Gharabaghi, B., Dickinson, W. T., Rudra, R. P., Snodgrass, W. J., & Krishnappan, B. G. (1999). Performance analysis of reinforced vegetative channel lining systems. Computers & Structures, 72(1-3), 149-164. https://doi.org/10.1016/S0045-7949(99)00005-X

Advances in Water Quality Modelling

• Pan, D., Deng, Y., Yang, S. X., & Gharabaghi, B. (2025). Recent Advances in Remote Sensing and Artificial Intelligence for River Water Quality Forecasting: A Review. Environments, 12(5), 158. https://doi.org/10.3390/environments12050158 
• Train, J., McBean, E., & Gharabaghi, B. (2024). Key Factors Influencing Drinking Water Advisories on Indigenous Reserves in Canada: An XGBoost Analysis. Water, 16(24), 3647. https://doi.org/10.3390/w16243647
• Safadoust, A., Soleymanekhtyari, S., & Gharabaghi, B. (2024). Zeolite intervention in soil nitrate dynamics: insights from column experiments and modelling. Hydrological Sciences Journal, 69(16), 2525-2536. https://doi.org/10.1080/02626667.2024.2413424
• Taraky, Y. M., McBean, E., Binns, A., & Gharabaghi, B. (2024). Advancing Water Security and Agricultural Productivity: A Case Study of Transboundary Cooperation Opportunities in the Kabul River Basin. Environments, 11(11), 253. https://doi.org/10.3390/environments11110253
• Deng, Y., Zhang, Y., Pan, D., Yang, S. X., & Gharabaghi, B. (2024). Review of recent advances in remote sensing and machine learning methods for lake water quality management. Remote Sensing, 16(22), 4196. https://doi.org/10.3390/rs16224196
• Oliveira Santos, V., Guimarães, B. M. D. M., Neto, I. E. L., de Souza Filho, F. D. A., Costa Rocha, P. A., Thé, J. V. G., & Gharabaghi, B. (2024). Chlorophyll-a estimation in 149 tropical semi-arid reservoirs using remote sensing data and six machine learning methods. Remote Sensing, 16(11), 1870. https://doi.org/10.3390/rs16111870
• Rocha, C., & Gharabaghi, B. (2024). Enhancing Turbidity Modeling in the Mississippi River Using Machine Learning and Sentinel‐2 Remote Sensing Data: A Generalizability Analysis. https://doi.org/10.20944/preprints202405.1259.v1
• Pan, D., Zhang, Y., Deng, Y., Van Griensven Thé, J., Yang, S. X., & Gharabaghi, B. (2024). Dissolved oxygen forecasting for lake Erie’s central basin using hybrid long short-term memory and gated recurrent unit networks. Water, 16(5), 707. https://doi.org/10.3390/w16050707
• Panjabi, K., Rudra, R. P., Shukla, R., Shrestha, N. K., Goel, P. K., Daggupati, P., & Gharabaghi, B. (2023). Spatiotemporal variability of minimum runoff generating areas: a field investigation. Hydrological Sciences Journal, 68(11), 1529-1538. https://doi.org/10.1080/02626667.2023.2195558
• Panjabi, K., Rudra, R., Goel, P., Ahmed, S., & Gharabaghi, B. (2021). A modified distributed CN-VSA method for mapping of the seasonally variable source areas. Water, 13(9), 1270. https://doi.org/10.3390/w13091270 
• Ahmadianfar, I., Kheyrandish, A., Jamei, M., & Gharabaghi, B. (2021). Optimizing operating rules for multi-reservoir hydropower generation systems: An adaptive hybrid differential evolution algorithm. Renewable Energy, 167, 774-790. https://doi.org/10.1016/j.renene.2020.11.152
• Taraky, Y. M., McBean, E., Liu, Y., Daggupati, P., Shrestha, N. K., Jiang, A., & Gharabaghi, B. (2021). The role of large dams in a transboundary drought management co-operation framework—Case study of the Kabul River Basin. Water, 13(19), 2628. https://doi.org/10.3390/w13192628
• Douglas-Mankin, K. R., Roy, S. K., Sheshukov, A. Y., Biswas, A., Gharabaghi, B., Binns, A.,  & Daggupati, P. (2020). A comprehensive review of ephemeral gully erosion models. Catena, 195, 104901. https://doi.org/10.1016/j.catena.2020.104901
• Gupta, A. K., Rudra, R. P., Gharabaghi, B., Daggupati, P., Goel, P. K., & Shukla, R. (2019). CoBAGNPS: A toolbox for simulating water and sediment control basin, WASCoB through AGNPS model. Catena, 179, 49-65. https://doi.org/10.1016/j.catena.2019.02.003
• Safadoust, A., Amiri Khaboushan, E., Mahboubi, A. A., Gharabaghi, B., Mosaddeghi, M. R., Ahrens, B., & Hassanpour, Y. (2016). Comparison of three models describing bromide transport affected by different soil structure types. Archives of Agronomy and Soil Science, 62(5), 674-687. https://doi.org/10.1080/03650340.2015.1074184
• Safadoust, A., Doaei, N., Mahboubi, A. A., Mosaddeghi, M. R., Gharabaghi, B., Voroney, P., & Ahrens, B. (2016). Long-term cultivation and landscape position effects on aggregate size and organic carbon fractionation on surface soil properties in semi-arid region of Iran. Arid Land Research and Management, 30(4), 345-361. https://doi.org/10.1080/15324982.2015.1016244
• Liu, Y., Yang, W., Yu, Z., Lung, I., & Gharabaghi, B. (2015). Estimating sediment yield from upland and channel erosion at a watershed scale using SWAT. Water resources management, 29(5), 1399-1412. https://doi.org/10.1007/s11269-014-0729-5
• Chapi, K., Rudra, R. P., Ahmed, S. I., Khan, A. A., Gharabaghi, B., Dickinson, W. T., & Goel, P. K. (2015). Spatial-temporal dynamics of runoff generation areas in a small agricultural watershed in southern Ontario. Journal of Water Resource and Protection, 7(1), 14-40. https://doi.org/10.4236/jwarp.2015.71002
• Gharabaghi, B., Safadoust, A., Mahboubi, A. A., Mosaddeghi, M. R., Unc, A., Ahrens, B., & Sayyad, G. (2015). Temperature effect on the transport of bromide and E. coli NAR in saturated soils. Journal of Hydrology, 522, 418-427. https://doi.org/10.1016/j.jhydrol.2015.01.003
• Sheklabadi, M., Mahmoudzadeh, H., Mahboubi, A. A., Gharabaghi, B., & Ahrens, B. (2015). Long-term land-use change effects on phosphorus fractionation in Zrêbar Lake margin soils. Archives of Agronomy and Soil Science, 61(6), 737-749. https://doi.org/10.1080/03650340.2014.954106
• Yousefi, G., Safadoust, A., Mahboubi, A. A., Gharabaghi, B., Mosaddeghi, M. R., Ahrens, B., & Shirani, H. (2014). Bromide and lithium transport in soils under long-term cultivation of alfalfa and wheat. Agriculture, Ecosystems & Environment, 188, 221-228. https://doi.org/10.1016/j.agee.2014.02.031
• Sheklabadi, M., Mahmoudzadeh, H., Mahboubi, A. A., Gharabaghi, B., & Ahrens, B. (2014). Land use effects on phosphorus sequestration in soil aggregates in western Iran. Environmental monitoring and assessment, 186(10), 6493-6503. https://doi.org/10.1007/s10661-014-3869-4
• Safadoust, A., Feizee, P., Mahboubi, A. A., Gharabaghi, B., Mosaddeghi, M. R., & Ahrens, B. (2014). Least limiting water range as affected by soil texture and cropping system. Agricultural Water Management, 136, 34-41. https://doi.org/10.1016/j.agwat.2014.01.007
• Arshad, R. R., Sayyad, G., Mosaddeghi, M., & Gharabaghi, B. (2013). Predicting saturated hydraulic conductivity by artificial intelligence and regression models. ISRN Soil Science, 2013. https://doi.org/10.1155/2013/308159
• Safadoust, A., Mahboubi, A. A., Mosaddeghi, M. R., Gharabaghi, B., Unc, A., Voroney, P., & Heydari, A. (2012). Effect of regenerated soil structure on unsaturated transport of Escherichia coli and bromide. Journal of Hydrology, 430, 80-90. https://doi.org/10.1016/j.jhydrol.2012.02.003
• Safadoust, A., Mahboubi, A. A., Mosaddeghi, M. R., Gharabaghi, B., Voroney, P., Unc, A., & Khodakaramian, G. (2012). Significance of physical weathering of two-texturally different soils for the saturated transport of Escherichia coli and bromide. Journal of Environmental Management, 107, 147-158. https://doi.org/10.1016/j.jenvman.2012.04.007
• Ahmed, S. I., Rudra, R. P., Gharabaghi, B., Mackenzie, K., & Dickinson, W. T. (2012). Within-storm rainfall distribution effect on soil erosion rate. ISRN Soil Science, 2012. https://doi.org/10.13031/2013.39243  
• Safadoust, A., Mahboubi, A. A., Gharabaghi, B., Mosaddeghi, M. R., Voroney, P., Unc, A., & Sayyad, G. (2011). Bacterial filtration rates in repacked and weathered soil columns.Geoderma, 167, 204-213. https://doi.org/10.1016/j.geoderma.2011.08.014
• Ahmed, I., Rudra, R., McKague, K., Gharabaghi, B., & Ogilvie, J. (2007). Evaluation of the root zone water quality model (RZWQM) for southern ontario: part I. Sensitivity analysis, calibration, and validation. Water Quality Research Journal, 42(3), 202-218. https://doi.org/10.2166/wqrj.2007.024
• Ahmed, I., Rudra, R., McKague, K., Gharabaghi, B., & Ogilvie, J. (2007). Evaluation of the Root Zone Water Quality Model (RZWQM) for Southern Ontario: Part II. Simulating long-term effects of nitrogen management practices on crop yield and subsurface drainage water quality. Water Quality Research Journal, 42(3), 219-230. https://doi.org/10.2166/wqrj.2007.025
• Das, S., Rudra, R. P., Goel, P. K., Gharabaghi, B., & Gupta, N. (2006). Evaluation of AnnAGNPS in cold and temperate regions. Water science and technology, 53(2), 263-270. https://doi.org/10.2166/wst.2006.060
• Rudra, R. P., Gharabaghi, B., Gebremeskel, S., Das, S., & Bai, H. (2006). Hydrological and water quality modeling in the Ontario River basins: comparison of model results. WIT Transactions on Ecology and the Environment, 95. https://doi.org/10.2495/WP060171
• McKague, K., Rudra, R. P., Ahmed, S. I., Gharabaghi, B., & Ogilvie, J. R. (2006). Simulating effects of MERN and other BMPs on subsurface drainage water quality and crop yield in southern Ontario. Canadian Biosystems Engineering, 48, 1.31-1.40. EID: 2-s2.0-33846217011 Rudra, R. P., Dickinson, W. T., & Gharabaghi, B. (2005). Hydrological concepts critical for water quality modeling.International Agricultural Engineering Journal, 14(4), 245-253.

Solid Waste Management

• Jahanfar, A., Amirmojahedi, M., Gharabaghi, B., Dubey, B., McBean, E., & Kumar, D. (2017). A novel risk assessment method for landfill slope failure: Case study application for Bhalswa Dumpsite, India. Waste Management & Research, 35(3), 220-227. https://doi.org/10.1177/0734242X16686412
• Jahanfar, A., Gharabaghi, B., McBean, E. A., & Dubey, B. K. (2017). Municipal solid waste slope stability modeling: a probabilistic approach. Journal of Geotechnical and Geoenvironmental Engineering, 143(8), 04017035. https://doi.org/10.1061/(ASCE)GT.1943-5606.0001704
• De Stefano, M., Gharabaghi, B., Clemmer, R., & Jahanfar, M. A. (2016). Berm design to reduce risks of catastrophic slope failures at solid waste disposal sites. Waste Management & Research, 34(11), 1117-1125. https://doi.org/10.1177/0734242X16662332
• Jahanfar, A., Dubey, B., Gharabaghi, B., & Movahed, S. B. (2016). Landfill failure mobility analysis: a probabilistic approach. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 10, 476-484.  SOURCE-WORK-ID: 062616112227-259
• Gharabaghi, B., Singh, M. K., Inkratas, C., Fleming, I. R., & McBean, E. (2008). Comparison of slope stability in two Brazilian municipal landfills. Waste management, 28(9), 1509-1517. https://doi.org/10.1016/j.wasman.2007.07.006

Air Quality Management

• Santos, V. O., Rocha, P. A. C., & Gharabaghi, B. (2025). Optimizing the Architecture of a Quantum–Classical Hybrid Machine Learning Model for Forecasting Ozone Concentrations: Air Quality Management Tool for Houston, Texas. Atmosphere, 16(3), 255. https://doi.org/10.3390/atmos16030255
• Rocha, P. A. C., Santos, V. O., & Gharabaghi, B. (2025). Deep Learning for Atmospheric Modeling: A Proof of Concept Using Fourier Neural Operator on WRF Data to Accelerate Transient Wind Forecasting at Multiple Altitudes. https://doi.org/10.3390/atmos16040394
• Rocha, P. A. C., De Andrade, C. F., Bueno, A. V., & Lobo, F. L. (2023). Trends on Wind Speed Forecasting: Umbrella Review of the Last 5 Years. https://www.preprints.org/manuscript/202308.1578/v1
• Ebtehaj, I., Bonakdari, H., Gharabaghi, B., & Khelifi, M. (2023). Time-Series-Based Air Temperature Forecasting Based on the Outlier Robust Extreme Learning Machine. Environmental Sciences Proceedings, 25(1), 51.  https://doi.org/10.3390/ECWS-7-14236
• Oliveira Santos, V., Costa Rocha, P. A., Scott, J., Van Griensven Thé, J., & Gharabaghi, B. (2023). Spatiotemporal Air pollution forecasting in Houston-TX: a case study for ozone using deep graph neural networks. Atmosphere, 14(2), 308. https://doi.org/10.3390/atmos14020308
• Kia, S., Nambiar, M. K., Thé, J., Gharabaghi, B., & Aliabadi, A. A. (2022). Machine learning to predict area fugitive emission fluxes of ghgs from open-pit mines. Atmosphere, 13(2), 210. https://doi.org/10.3390/atmos13020210
• Gharabaghi, B. (2020). Estimating on-road vehicle density using crowdsourced data and Monte Carlo analysis.
• Aliabadi, A. A., Moradi, M., Clement, D., Lubitz, W. D., & Gharabaghi, B. (2019). Flow and temperature dynamics in an urban canyon under a comprehensive set of wind directions, wind speeds, and thermal stability conditions. Environmental Fluid Mechanics, 19(1), 81-109. https://doi.org/10.1007/s10652-018-9606-8
• Freeman, B., & Gharabaghi, B. (2019). Estimating annual air emissions from nargyla water pipes in cafés and restaurants using Monte Carlo analysis. International Journal of Environmental Science and Technology, 16(6), 2539-2548. https://doi.org/10.1007/s13762-018-1662-6
• Freeman, B. S., Al Matawah, J. A., Al Najjar, M., Gharabaghi, B., & Thé, J. (2019). Vehicle stacking estimation at signalized intersections with unmanned aerial systems. International Journal of Transportation Science and Technology, 8(2), 231-249. https://doi.org/10.1016/j.ijtst.2018.12.002
• Sattar, A. M., Elhakeem, M., Gerges, B. N., Gharabaghi, B., & Gultepe, I. (2018). Wind-Induced Air-Flow Patterns in an Urban Setting: Observations and Numerical Modeling. Pure and Applied Geophysics, 175(8), 3051-3068. https://doi.org/10.1007/s00024-018-1846-5
• Freeman, B. S., Taylor, G., Gharabaghi, B., & Thé, J. (2018). Forecasting air quality time series using deep learning. Journal of the Air & Waste Management Association, 68(8), 866-886. https://doi.org/10.1080/10962247.2018.1459956
• Freeman, B., Gharabaghi, B., Thé, J., Munshed, M., Faisal, S., Abdullah, M., & Al Aseed, A. (2017). Mapping air quality zones for coastal urban centers. Journal of the Air & Waste Management Association, 67(5), 565-581. https://doi.org/10.1080/10962247.2016.1265025
• Freeman, B., McBean, E., Gharabaghi, B., & Thé, J. (2017). Evaluation of air quality zone classification methods based on ambient air concentration exposure. Journal of the Air & Waste Management Association, 67(5), 550-564. https://doi.org/10.1080/10962247.2016.1263585
• Freeman, B., Gharabaghi, B., & Thé, J. (2015). Estimation of mixed traffic densities in congested road using Monte Carlo analysis. EM: Air & Waste Management Association’s magazine for environmental managers, April 2015:8-13.
• Freeman, B., Gharabaghi, B., Faisal, S., & Thé, J. (2015). Vehicle I/M Programs for Developing Nations. EM: Air & Waste Management Association’s magazine for environmental managers, April 2015:14-18.
• Weiss, L., Thé, J., Winter, J., & Gharabaghi, B. (2018). Optimizing best management practices to control anthropogenic sources of atmospheric phosphorus deposition to inland lakes. Journal of the Air & Waste Management Association, 68(10), 1025-1037. https://doi.org/10.1080/10962247.2018.1463929
• Weiss, L. (2018). A New Methodology to Manage Anthropogenic Sources of Phosphorus Deposition to Lakes: Lake Simcoe Case Study (Doctoral dissertation).
• Weiss, L., Gharabaghi, B., & Thé, J. (2015). Modelling and Management of PM10 from Mobile Sources. EM: Air & Waste Management Association’s magazine for environmental managers, April 2015:24-28.
• Weiss, L., Thé, J., Gharabaghi, B., Stainsby, E. A., & Winter, J. G. (2014). A new dust transport approach to quantify anthropogenic sources of atmospheric PM10 deposition on lakes. Atmospheric environment, 96, 380-392. https://doi.org/10.1016/j.atmosenv.2014.07.060
• Weiss, L., Stainsby, E. A., Gharabaghi, B., Thé, J., & Winter, J. G. (2013). Mapping key agricultural sources of dust emissions within the Lake Simcoe airshed. Inland Waters, 3(2), 153-166. https://doi.org/10.5268/IW-3.2.515
• Brown, L. J., Taleban, V., Gharabaghi, B., & Weiss, L. (2011). Seasonal and spatial distribution patterns of atmospheric phosphorus deposition to Lake Simcoe, ON. Journal of Great Lakes Research, 37, 15-25. https://doi.org/10.1016/j.jglr.2011.01.004
• Ramkellawan, J., Gharabaghi, B., & Winter, J. G. (2009). Application of weather radar in estimation of bulk atmospheric deposition of total phosphorus over Lake Simcoe. Canadian Water Resources Journal, 34(1), 37-60. https://doi.org/10.4296/cwrj3401037

Climate Variability and Change

• Elkurdy, M., Binns, A., & Gharabaghi, B. (2025). Decomposing streamflow for improved river flow prediction accuracy of machine learning models. International Journal of River Basin Management, 1-16. https://doi.org/10.1080/15715124.2024.2444680
• Livingston, T., McBean, E., Marchildon, M., & Gharabaghi, B. (2021). Hydrologic impacts of climate change in relation to Ontario’s source water protection planning program. Canadian Journal of Civil Engineering, 48(8), 1037-1045. https://doi.org/10.1139/cjce-2019-0649
• Nalley, D., Adamowski, J., Biswas, A., Gharabaghi, B., & Hu, W. (2019). A multiscale and multivariate analysis of precipitation and streamflow variability in relation to ENSO, NAO and PDO. Journal of Hydrology, 574, 288-307. https://doi.org/10.1016/j.jhydrol.2019.04.024
• Wang, Y., McBean, E., & Gharabaghi, B. (2018). Increased risks of waterborne disease outbreaks in northern Ontario due to climate change. Journal of Water Management Modeling. https://doi.org/10.14796/jwmm.c436
• Atieh, M., Rudra, R., Gharabaghi, B., & Lubitz, D. (2017). Investigating the Spatial and Temporal Variability of Precipitation using Entropy Theory. Journal of Water Management Modeling. https://doi.org/10.14796/jwmm.c420
• Asnaashari, A., Gharabaghi, B., McBean, E., & Mahboubi, A. A. (2015). Reservoir management under predictable climate variability and change. Journal of Water and Climate Change, 6(3), 472-485. https://doi.org/10.2166/wcc.2015.053
• Rudra, R., Dickinson, W.T., Ahmed, S. I., Patel, P., Zhou, J., & Gharabaghi, B. (2015).  Changes in Rainfall Extremes in Ontario.   International Journal of Environmental Research, 9 (4), 1117-1372. https://doi.org/10.22059/ijer.2015.1000
• Vasiljevic, B., McBean, E., & Gharabaghi, B. (2012). Trends in rainfall intensity for stormwater designs in Ontario. Journal of Water and Climate Change, 3(1), 1-10. https://doi.org/10.2166/wcc.2012.125
• McBean, E., deJong, A., & Gharabaghi, B. (2011). Groundwater in Bangladesh: implications in a climate-changing world. Water Res Manage, 1(3), 3-8.
• deJong, A., McBean, E., & Gharabaghi, B. (2010). Projected climate conditions to 2100 for Regina, Saskatchewan. Canadian Journal of Civil Engineering, 37(9), 1247-1260. https://doi.org/10.1139/L10-061 McKague, K., Rudra, R., Ogilvie, J., Ahmed, I., & Gharabaghi, B. (2005). Evaluation of weather generator ClimGen for southern Ontario.Canadian Water Resources Journal, 30(4), 315-330. https://doi.org/10.4296/cwrj3004315

Coastal Engineering

• Ahmadianfar, I., Jamei, M., Karbasi, M., Sharafati, A., & Gharabaghi, B. (2022). A novel boosting ensemble committee-based model for local scour depth around non-uniformly spaced pile groups. Engineering with Computers, 38(4), 3439-3461. https://doi.org/10.1007/s00366-021-01370-2
• Bonakdari, H., Ebtehaj, I., Azimi, A. H., Samui, P., Sattar, A. A., Jamali, A.,  & Gharabaghi, B. (2021). Pareto design of multiobjective evolutionary neuro-fuzzy system for predicting scour depth around bridge piers. In Water Engineering Modeling and Mathematic Tools (pp. 491-517). Elsevier. https://doi.org/10.1016/B978-0-12-820644-7.00012-8
• Bonakdari, H., Moradi, F., Ebtehaj, I., Gharabaghi, B., Sattar, A. A., Azimi, A. H., & Radecki-Pawlik, A. (2020). A non-tuned machine learning technique for abutment scour depth in clear water condition. Water, 12(1), 301. https://doi.org/10.3390/w12010301
• Moradi, F., Bonakdari, H., Kisi, O., Ebtehaj, I., Shiri, J., & Gharabaghi, B. (2019). Abutment scour depth modeling using neuro-fuzzy-embedded techniques. Marine Georesources & Geotechnology, 37(2), 190-200. https://doi.org/10.1080/1064119X.2017.1420113
• Power, H. E., Gharabaghi, B., Bonakdari, H., Robertson, B., Atkinson, A. L., & Baldock, T. E. (2019). Prediction of wave runup on beaches using Gene-Expression Programming and empirical relationships. Coastal Engineering, 144, 47-61. https://doi.org/10.1016/j.coastaleng.2018.10.006
• Robertson, B., Gharabaghi, B., & Power, H. E. (2017). Predicting breaking wave conditions using gene expression programming. Coastal Engineering Journal, 59(03), 1750017. https://doi.org/10.1142/S0578563417500176
• Robertson, B., Gharabaghi, B., & Hall, K. (2015). Prediction of incipient breaking wave-heights using artificial neural networks and empirical relationships. Coastal Engineering Journal, 57(04), 1550018. https://doi.org/10.1142/S0578563415500187
• Ebtehaj, I., Bonakdari, H., Moradi, F., Gharabaghi, B., & Khozani, Z. S. (2018). An integrated framework of Extreme Learning Machines for predicting scour at pile groups in clear water condition. Coastal Engin

Energy Conservation and Sustainability

• Hasan, Y., Hasan, I., Aliabadi, A. A., & Gharabaghi, B. (2025). Comparative Life Cycle Assessment (LCA) in the Aerospace Industry Regarding Aviation Seat Frame Options. Sustainability, 17(7), 3188. https://doi.org/10.3390/su17073188
• Santos, V. O., Marinho, F. P., Rocha, P. A. C., & Gharabaghi, B. (2024). Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California. Energies, 17(14), 1-26. https://doi.org/10.3390/en17143580
• Safdari, M., Dennis, K., Gharabaghi, B., Siddiqui, K., & Aliabadi, A. A. (2024). Implications of latent and sensible building energy loads using natural ventilation. Journal of Building Engineering, 96, 110447. https://doi.org/10.1016/j.jobe.2024.110447
• Rosenfield, A., Ficht, A., Lyons, E. M., & Gharabaghi, B. (2024). A One-Dimensional Light Detection and Ranging Array Scanner for Mapping Turfgrass Quality. Remote Sensing, 16(12), 2215. https://doi.org/10.3390/rs16122215
• Vidal Bezerra, F. D., Pinto Marinho, F., Costa Rocha, P. A., Oliveira Santos, V., Van Griensven Thé, J., & Gharabaghi, B. (2023). Machine learning dynamic ensemble methods for solar irradiance and wind speed predictions. Atmosphere, 14(11), 1635.  https://doi.org/10.3390/atmos14111635
• Santos, V. O., Rocha, P. A. C., Scott, J., Thé, J. V. G., & Gharabaghi, B. (2023). Spatiotemporal analysis of bidimensional wind speed forecasting: Development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database. Energy, 278, 127852. https://doi.org/10.1016/j.energy.2023.127852