Machine Learning Application in Water Quality Using Satellite Data

Abstract

Abstract Monitoring water quality is a critical aspect of environmental sustainability. Poor water quality has an impact not just on aquatic life but also on the ecosystem. The purpose of this systematic review is to identify peer-reviewed literature on the effectiveness of applying machine learning (ML) methodologies to estimate water quality parameters with satellite data. The data was gathered using the Scopus, Web of Science, and IEEE citation databases. Related articles were extracted, selected, and evaluated using advanced keyword search and the PRISMA approach. The bibliographic information from publications written in journals during the previous two decades were collected. Publications that applied ML to water quality parameter retrieval with a focus on the application of satellite data were identified for further systematic review. A search query of 1796 papers identified 113 eligible studies. Popular ML models application were artificial neural network (ANN), random forest (RF), support vector machines (SVM), regression, cubist, genetic programming (GP) and decision tree (DT). Common water quality parameters extracted were chlorophyll-a (Chl-a), temperature, salinity, colored dissolved organic matter (CDOM), suspended solids and turbidity. According to the systematic analysis, ML can be successfully extended to water quality monitoring, allowing researchers to forecast and learn from natural processes in the environment, as well as assess human impacts on an ecosystem. These efforts will also help with restoration programs to ensure that environmental policy guidelines are followed.