Sustainable Management of Water and Environment with the Aid of Advanced Computing Methods

Dear Colleagues,

For the accurate management of water resources, prediction and/or estimation of the nonlinear phenomena (e.g., the parameters related to hydrological cycle) are mostly required. With the effect of climate change and population growth in most parts of the world, finding a solution for such problems is much more challenging, and this problem can be addressed through the use of advanced computational tools. The rational management of a city and its infrastructure in response to increased pollution, climate change, and natural and other disasters, for daily operation and emergency response, is becoming critical to enhance livability for citizens. Creating healthy, sustainable urban environments necessitates advanced numerical tools for optimal design and management processes. Extreme weather events cause numerous economic and life losses in the changing climate and environment. It is, therefore, important to keep developing and improving our knowledge in the field of extreme weather vulnerability assessment and hazard alleviation. 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 the management of water resources. The topics of this Special Issue include but are not limited to:

• Forecasting sustainable water resources variables  (modeling streamflow, sediment, groundwater, lake level, evaporation, evapotranspiration etc.) with advanced MLM;
• Optimization of available water resources with advanced computing methods;
• Probabilistic and susceptibility studies with artificial intelligence to sustain water resources;
• Spatial and temporal extreme events modeling with novel models to conserve water resources;
• Implementation of MLM with new metaheuristic algorithms in water resources;
• Reservoir operation using Mas;
• Ensemble modeling procedure with MLM in water resources;
• Application of conjunction MLM such as wavelet or EEMD-based MLM.

Prof. Dr. Ozgur Kisi
Dr. Rana Muhammad Adnan
Guest Editors

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