| Multi-time scale stream flow predictions: The support vector machines approach T Asefa, M Kemblowski, M McKee, A Khalil Journal of hydrology 318 (1-4), 7-16, 2006 | 345 | 2006 |
| SOIL MOISTURE PREDICTION USING SUPPORT VECTOR MACHINES1 MK Gill, T Asefa, MW Kemblowski, M McKee JAWRA Journal of the American Water Resources Association 42 (4), 1033-1046, 2006 | 297 | 2006 |
| Performance evaluation of a water resources system under varying climatic conditions: Reliability, Resilience, Vulnerability and beyond T Asefa, J Clayton, A Adams, D Anderson Journal of Hydrology 508, 53-65, 2014 | 165 | 2014 |
| Support vectors–based groundwater head observation networks design T Asefa, MW Kemblowski, G Urroz, M McKee, A Khalil Water Resources Research 40 (11), 2004 | 113 | 2004 |
| Effect of missing data on performance of learning algorithms for hydrologic predictions: Implications to an imputation technique MK Gill, T Asefa, Y Kaheil, M McKee Water resources research 43 (7), 2007 | 97 | 2007 |
| Multiobjective analysis of chaotic dynamic systems with sparse learning machines AF Khalil, M McKee, M Kemblowski, T Asefa, L Bastidas Advances in water resources 29 (1), 72-88, 2006 | 91 | 2006 |
| Support vector machines for nonlinear state space reconstruction: Application to the Great Salt Lake time series T Asefa, M Kemblowski, U Lall, G Urroz Water resources research 41 (12), 2005 | 67 | 2005 |
| Sparse Bayesian learning machine for real‐time management of reservoir releases A Khalil, M McKee, M Kemblowski, T Asefa Water Resources Research 41 (11), 2005 | 59 | 2005 |
| Support vector machines (SVMs) for monitoring network design T Asefa, M Kemblowski, G Urroz, M McKee Groundwater 43 (3), 413-422, 2005 | 58 | 2005 |
| Improving short-term urban water demand forecasts with reforecast analog ensembles D Tian, CJ Martinez, T Asefa Journal of Water Resources Planning and Management 142 (6), 04016008, 2016 | 36 | 2016 |
| BASIN SCALE WATER MANAGEMENT AND FORECASTING USING ARTIFICIAL NEURAL NETWORKS1 AF Khalil, M McKee, M Kemblowski, T Asefa JAWRA Journal of the American Water Resources Association 41 (1), 195-208, 2005 | 32 | 2005 |
| Proactive water shortage mitigation integrating system optimization and input uncertainty H Wang, T Asefa, D Bracciano, A Adams, N Wanakule Journal of hydrology 571, 711-722, 2019 | 27 | 2019 |
| Impact of different types of ENSO conditions on seasonal precipitation and streamflow in the Southeastern United States H Wang, T Asefa International Journal of Climatology 38 (3), 1438-1451, 2018 | 26 | 2018 |
| Evaluation of impacts of future climate change and water use scenarios on regional hydrology S Chang, W Graham, J Geurink, N Wanakule, T Asefa Hydrology and Earth System Sciences 22 (9), 4793-4813, 2018 | 25 | 2018 |
| Evaluation of water saving potential for short-term water demand management H Wang, D Bracciano, T Asefa Water Resources Management 34 (10), 3317-3330, 2020 | 23 | 2020 |
| A tale of integrated regional water supply planning: Meshing socio-economic, policy, governance, and sustainability desires together T Asefa, A Adams, I Kajtezovic-Blankenship Journal of hydrology 519, 2632-2641, 2014 | 22 | 2014 |
| Field‐Scale Application of Three Types of Neural Networks to Predict Ground‐Water Levels1 T Asefa, N Wanakule, A Adams JAWRA Journal of the American Water Resources Association 43 (5), 1245-1256, 2007 | 17 | 2007 |
| Support vector machines approximation of flow and transport models in initial groundwater contamination network design T Asefa, MW Kemblowski Eos Trans. AGU 83 (47), 2002 | 17 | 2002 |
| Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1 T Asefa JAWRA Journal of the American Water Resources Association 45 (5), 1155-1163, 2009 | 16 | 2009 |
| Evaluating the potential impact of climate change on the hydrology of Ribb catchment, Lake Tana Basin, Ethiopia DW Ayalew, T Asefa, MA Moges, SM Leyew Journal of Water and Climate Change 13 (1), 190-205, 2022 | 15 | 2022 |
MW KemblowskiProfessor of Civil and Environmental EngineeringVerified email at pg.gda.pl
