Machine Learning with Metaheuristic Algorithms for Sustainable Water Resources Management

Dear Colleagues,

Water resources management (WR) mostly necessitates the prediction or estimation of the nonlinear phenomena (e.g., the parameters related to hydrological cycle). With climate change and population growth in the most parts of the world, the solution of such problems necessitates advanced computational tools. The main aim of this Special Issue is to explore various implementations of machine learning methods (MLM) improved with metaheuristic algorithms (MAs) to advance prediction and/or modeling hydrological/water resources phenomena which have vital importance in management of water resources. The topics of this Special Issue include, but are not limited to:

• Hydrologic forecasting (modeling streamflow, sediment, groundwater, lake level, evaporation, evapotranspiration etc.) with advanced MLM
• Implementation of MLM with new metaheuristic algorithms in WR
• Reservoir operation using MAs
• Ensemble modeling procedure with MLM in WR
• Application of conjunction MLM such as wavelet or EEMD based MLM

Prof. Ozgur Kisi
Guest Editor

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