| Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study D Rincón, UT Khan, C Armenakis Geosciences 8 (8), 275, 2018 | 204 | 2018 |
| Bioretention cell efficacy in cold climates: Part 1—hydrologic performance UT Khan, C Valeo, A Chu, B Van Duin Canadian Journal of Civil Engineering 39 (11), 1210-1221, 2012 | 94 | 2012 |
| A comprehensive review of low impact development models for research, conceptual, preliminary and detailed design applications S Kaykhosravi, UT Khan, A Jadidi Water 10 (11), 1541, 2018 | 92 | 2018 |
| A comprehensive comparison of four input variable selection methods for artificial neural network flow forecasting models E Snieder, R Shakir, UT Khan Journal of Hydrology 583, 124299, 2020 | 67 | 2020 |
| An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design J Morgenroth, UT Khan, MA Perras Geosciences 9 (12), 504, 2019 | 67 | 2019 |
| Bioretention cell efficacy in cold climates: Part 2—water quality performance UT Khan, C Valeo, A Chu, B Van Duin Canadian Journal of Civil Engineering 39 (11), 1222-1233, 2012 | 57 | 2012 |
| A new fuzzy linear regression approach for dissolved oxygen prediction UT Khan, C Valeo Hydrological Sciences Journal 60 (6), 1096-1119, 2015 | 53 | 2015 |
| River flood prediction using fuzzy neural networks: an investigation on automated network architecture UT Khan, J He, C Valeo Water Science and Technology 2017 (1), 238-247, 2018 | 42 | 2018 |
| The low-impact development demand index: a new approach to identifying locations for LID S Kaykhosravi, K Abogadil, UT Khan, MA Jadidi Water 11 (11), 2341, 2019 | 38 | 2019 |
| Short-Term Peak Flow Rate Prediction and Flood Risk Assessment Using Fuzzy Linear Regression. UT Khan, C Valeo Journal of Environmental Informatics 28 (2), 2016 | 38 | 2016 |
| A data driven approach to bioretention cell performance: prediction and design UT Khan, C Valeo, A Chu, J He Water 5 (1), 13-28, 2013 | 37* | 2013 |
| Comparing A Bayesian and Fuzzy Number Approach to Uncertainty Quantification in Short-Term Dissolved Oxygen Prediction. UT Khan, C Valeo Journal of Environmental Informatics 30 (1), 2017 | 36 | 2017 |
| Dissolved oxygen prediction using a possibility-theory based fuzzy neural network UT Khan, C Valeo Hydrol. Earth Syst. Sci. 20 (6), 2267-2293, 2016 | 34 | 2016 |
| The effect of climate change and urbanization on the demand for low impact development for three Canadian cities S Kaykhosravi, UT Khan, MA Jadidi Water 12 (5), 1280, 2020 | 27 | 2020 |
| Non-linear fuzzy-set based uncertainty propagation for improved DO prediction using multiple-linear regression UT Khan, C Valeo, J He Stochastic environmental research and risk assessment 27, 599-616, 2013 | 27 | 2013 |
| Resampling and ensemble techniques for improving ANN-based high-flow forecast accuracy E Snieder, K Abogadil, UT Khan Hydrology and Earth System Sciences 25 (5), 2543–2566, 2021 | 23 | 2021 |
| Effects of urban form on wadi flow frequency analysis in the Wadi Aday watershed in Muscat, Oman GA Al-Rawas, C Valeo, UT Khan, OH Al-Hafeedh Urban Water Journal 12 (4), 263-274, 2015 | 21 | 2015 |
| Optimising fuzzy neural network architecture for dissolved oxygen prediction and risk analysis UT Khan, C Valeo Water 9 (6), 381, 2017 | 18 | 2017 |
| Stochastic flood risk assessment under climate change scenarios for Toronto, Canada using CAPRA D Rincón, JF Velandia, I Tsanis, UT Khan Water 14 (2), 227, 2022 | 14 | 2022 |
| Subsurface transport of carboxymethyl cellulose (CMC)-stabilized nanoscale zero valent iron (nZVI): Numerical and statistical analysis MA Asad, UT Khan, MM Krol Journal of Contaminant Hydrology 243, 103870, 2021 | 12 | 2021 |
Caterina ValeoUniversity of Victoria, University of Calgary在 uvic.ca 的电子邮件经过验证
