| 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 | 62 | 2020 |
| 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 | 19 | 2021 |
| Data driven models as a powerful tool to simulate emerging bioprocesses: An artificial neural network model to describe methanotrophic microbial activity A AlSayed, M Soliman, R Shakir, E Snieder, A ElDyasti, UT Khan International Society for Environmental Information Sciences, 2021 | 7 | 2021 |
| Resampling and ensemble techniques for improving ANN-based high streamflow forecast accuracy E Snieder, K Abogadil, UT Khan Hydrol. Earth Syst. Sci. Discuss, 1-35, 2020 | 5 | 2020 |
| A novel ensemble algorithm based on hydrological event diversity for urban rainfall–runoff model calibration and validation E Snieder, UT Khan Journal of Hydrology 619, 129193, 2023 | 3 | 2023 |
| Comparison of Bayesian Belief Networks and Artificial Neural Networks for prediction of tunnel ground class J Morgenroth, E Snieder, M Perras, UT Khan ISRM Congress, ISRM-14CONGRESS-2019-291, 2019 | 2 | 2019 |
| Large-scale evaluation of temporal trends in ANN behaviour for daily flow forecasts in Canadian catchments. E Snieder, U Khan EGU General Assembly Conference Abstracts, EGU22-10744, 2022 | 1 | 2022 |
| Surrogate Model Development for Bioretention Cell Simulation-Optimisation Applications R Khalid, E Snieder, UT Khan Canadian Society of Civil Engineering Annual Conference, 197-210, 2021 | 1 | 2021 |
| A comprehensive evaluation of boosting algorithms for artificial neural network-based flow forecasting models E Snieder, UT Khan Proceedings of the AGU Fall Meeting, 2019 | 1 | 2019 |
| A Comparison of Two Data-Driven Models to Predict Hypolimnetic Dissolved Oxygen Concentration: A Case Study of the Seymareh Reservoir in Iran AK Nokhandan, E Snieder, UT Khan, A ElDyasti, Z Ghaemi, M Bagheri J. Environ. Inform. Lett 2, 70-81, 2019 | 1 | 2019 |
| Comparison of four input variable selection methods for artificial neural network based flood forecasting models E Snieder, R Shakir, UT Khan Proceedings, Annual Conference-Canadian Society for Civil Engineering, 1-10, 2019 | 1 | 2019 |
| Artificial neural network-based flood forecasting: Input variable selection and peak flow prediction accuracy EJ Snieder | 1 | 2019 |
| Towards improved spatio-temporal selection of training data for LSTM-based flow forecasting models in Canadian basins E Snieder, U Khan EGU24, 2024 | | 2024 |
| A large sample study of the effects of upstream hydrometeorological input features for LSTM-based daily flow forecasting in Canadian catchments E Snieder, U Khan EGU General Assembly Conference Abstracts, EGU-8746, 2023 | | 2023 |
| Cluster-based hyperparameter optimisation for LSTM-based flow forecasting in Canadian catchments UT Khan, E Snieder EGU General Assembly Conference Abstracts, EGU-9114, 2023 | | 2023 |
| Evaluation of Process-Based Ensemble Models for Forecasting Point-of-Consumption Free Residual Chlorine in Refugee Settlements M De Santi, E Snieder, SI Ali, UT Khan, JF Fesselet, J Orbinski Canadian Society of Civil Engineering Annual Conference, 951-966, 2022 | | 2022 |
| Large-scale analysis of machine learning based flow forecasting models for Southern Ontario U Khan, E Snieder AGU Fall Meeting Abstracts 2021, H25A-1059, 2021 | | 2021 |
| Investigating Event Selection for GA-Based SWMM Rainfall-Runoff Model Calibration E Snieder, UT Khan Canadian Society of Civil Engineering Annual Conference, 429-441, 2021 | | 2021 |
| Spatial bootstrapping for model-free estimation of subcatchment parameter uncertainty for a semi-distributed rainfall runoff model E Snieder, U Khan EGU21, 2021 | | 2021 |
| Improved real-time SWMM flow forecasts using two machine learning approaches E Snieder, A Shahmansouri, CH Cheng, Y Ding, E Graham, U Khan EGU General Assembly Conference Abstracts, 845, 2020 | | 2020 |
Usman T KhanYork UniversityE-mail megerősítve itt: yorku.ca
