Advanced Computing Methods for Environmental Sustainability

Dear Colleagues,

The rational management of a city and its infrastructure in response to increased pollution, climate change, natural and other disasters, for daily operation and emergency responses, 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 (MLMs) improved with metaheuristic algorithms (MAs) to advance the prediction and/or modeling of environmental variables, which have vital importance in sustainable and resilient environments. The topics of this Special Issue include, but are not limited to, the following:

• Forecasting of sustainable environmental variables (modeling streamflow, sediment, groundwater, lake level, evaporation, evapotranspiration, etc.) with advanced MLM;
• Optimization of available environmental variables with advanced computing methods;
• Probalistic and suspectibility studies with artifical intelligence to sustain environmental resources;
• Spatial and temporal extreme events modeling with novel models to conserve environmental resources (ER);
• Implementation of MLM with new metaheuristic algorithms in environmental variables Modeling.

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

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