Water Resources Management: Advances in Machine Learning Approaches

Dear Colleagues

Water resources management at the catchment level is a scientific discipline with great environmental importance. It is a multidisciplinary issue which has prevailed from the cooperation of a wide range of scientists, such as engineers, Earth scientists, agronomists, environmentalists, biologists, and economists. The target is the optimal distribution of limited water resources and the preservation of acceptable levels of water quality, in such a way that all the users’ needs in domestic, agricultural, industrial and ecological uses are satisfied with the least controversy and conflicts.

In order to achieve operational and efficient water management, we need to have reliable monitoring time-series data and the appropriate tools for their processing.

Machine learning approaches are a very powerful tool for the simulation, prediction, optimization, assessment and management of catchment water resources. For many decades, a high number of both deterministic and stochastic models for the simulation and optimization of catchment water resources have been very successfully applied. Machine learning approaches are the first step of artificial intelligence, and can give more integrated answers to both quantitative and qualitative water management problems at the catchment level:

a) Water quantity management: evapotranspiration models; water balance models; land cover and land use; agricultural, domestic, industrial and environmental use of water.

b) Water quality management: water temperature; dissolved oxygen; chlorophyll-a; electrical conductivity of water; eutrophication indexes; rivers, lakes, wetlands, deltas, internal and transitional waters.

Prof. Dr. Aris Psilovikos
Guest Editor

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