Application of Machine Learning to Water Resource Modeling

Dear Colleagues,

Water shortages have become a severe problem in many parts of the world. Although a variety of artificial interventions have been used to deal with water shortages, they have brought a series of ecological and environmental problems such as groundwater table decline, vegetation degradation, land desertification, and water quality deterioration, posing great challenges for water resource management. In recent decades, a series of models and tools have been developed for water resource simulation, optimization, and management. However, with the continuous intensification of human activities, the water resource system has gradually become a typical “human–natural coupling system” which interacts with human activities and natural processes. The complex interaction and nonlinear relationship between human systems and natural systems make these natural system-based water resource management tools and models unable to accurately capture and reflect the evolution process of the water resources system. Therefore, the question of how to analyze the water resources system comprehensively and systematically and accurately describe the hydrology and water cycle process under the dual drive of the human system and the natural system has become a hot spot and difficult problem in the current water resource management research. This Special Issue aims to collect and share innovative ideas and applications of machine learning to water resource modeling. We are pleased to invite authors to publish original research articles, review articles, and short communications on relevant topics.

Dr. Ze Liu
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.