A review of machine learning, remote sensing, and statistical methods for reservoir water quality assessment

Abstract

Water reservoirs perform a number of essential functions, including water supply, flood control, hydropower generation, and agricultural and industrial support. In order to meet specific standards, the reservoir water quality needs to be protected. Because of human activities, including industrial discharges and agricultural runoff, reservoir’s water quality deteriorates. Deforestation and erosion in the upstream region exacerbate the problem, disrupting the ecology. A comprehensive management practice is necessary to maintain reservoir water quality in addition to changes in flow patterns, temperature changes, and nutrient enrichment. A number of methods have been employed, including Remote Sensing (RS) for spatial monitoring of environmental change, Machine Learning (ML) for estimation/predicting water quality, and Multivariate Statistical Analysis (MSA) that can identify relationships among water quality variables and patterns. By examining the strengths of these methods, it is possible to maximize the effectiveness of reservoir management. For instance, by understanding each method, it is possible to identify the optimal combination of techniques to achieve the best results. Furthermore, it addresses a wide range of challenges related to assessing water quality and ecosystem health. The use of one or more of these approaches will depend on the objectives, data characteristics, and resources available. Additionally, it can be used to identify and mitigate the risks associated with reservoir management. The articles in this review paper were limited to those published between 2000 and 2023, with a reasonable geographical distribution based on our literature search in the SCOPUS database.