Artificial intelligence methods in water systems research – a literature review

Julia Piotrowska, Dominika Dąbrowska

Abstract


We overview selected artificial intelligence methods used in research on water systems, specifically artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), genetic programming (GP) and support vector machine (SVM) methods. Each method is characterized and the most effective ways of using these methods are discussed. These methods prove widely useful in forecasting changes in selected surface and groundwater quality parameters, forecasting sewage network failures, assessing water treatment options, climate monitoring, drought detection and environmental issues for farmers and producers. Published studies show that artificial intelligence methods should be used in the analysis of water systems, especially since artificial intelligence now appears in search results for over 60,000 environmental articles.


Keywords


hydrogeology; machine learning; urban water systems; artificial intelligence; sensors; analysis

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