Tirusew Asefa
Tirusew Asefa
Manager, Planning & System Decision Support, Tampa Bay Water, Courtesy Professor at USF
Verified email at tampabaywater.org
Title
Cited by
Cited by
Year
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
2412006
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
1632006
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
922014
Support vectors–based groundwater head observation networks design
T Asefa, MW Kemblowski, G Urroz, M McKee, A Khalil
Water Resources Research 40 (11), 2004
872004
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
732006
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
492005
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
462005
Support vector machines (SVMs) for monitoring network design
T Asefa, M Kemblowski, G Urroz, M McKee
Groundwater 43 (3), 413-422, 2005
412005
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
372007
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
292005
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
172002
Ensemble Streamflow Forecast: A GLUE‐Based Neural Network Approach1
T Asefa
JAWRA Journal of the American Water Resources Association 45 (5), 1155-1163, 2009
162009
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
142016
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
122007
Reducing bias-corrected precipitation projection uncertainties: a Bayesian-based indicator-weighting approach
T Asefa, A Adams
Regional Environmental Change 13 (1), 111-120, 2013
112013
Open integration of a spatial water balance model and GIS to study the response of a catchment
T Asefa, Z Wang, O Batelaan, F De Smedt
Proceedings of the 19th Annual American Geophysical Union Hydrology Days …, 1999
91999
On the use of system performance metrics for assessing the value of incremental water-use permits
T Asefa, N Wanakule, A Adams, J Shelby, J Clayton
Journal of Water Resources Planning and Management 140 (7), 04014012, 2014
82014
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
72018
A Level‐of‐Service Concept for Planning Future Water Supply Projects under Probabilistic Demand and Supply Framework
T Asefa, A Adams, N Wanakule
JAWRA Journal of the American Water Resources Association 51 (5), 1272-1285, 2015
72015
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
72014
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Articles 1–20