Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature review

Abstract

Sustainable water quality underpins ecological balance and water security. Assessing and managing lakes and reservoirs is difficult due to data sparsity, heterogeneity, and nonlinear relationships among parameters. This review examines how Self-Organizing Map (SOM), an unsupervised AI technique, is applied to water quality assessment. It synthesizes research on parameter selection, spatial and temporal sampling strategies, and clustering approaches. Emphasis is placed on how SOM handles multidimensional data and uncovers hidden patterns to support effective water management. The growing availability of environmental data from in-situ sensors, remote sensing imagery, IoT technologies, and historical records has significantly expanded analytical opportunities in environmental monitoring. SOM has proven effective in analysing complex datasets, particularly when labelled data are limited or unavailable. It enables high-dimensional data visualization, facilitates the detection of hidden ecological patterns, and identifies critical correlations among diverse water quality indicators. This review highlights SOMs versatility in ecological assessments, trophic state classification, algal bloom monitoring, and catchment area impact evaluations. The findings offer comprehensive insights into existing methodologies, supporting future research and practical applications aimed at improving the monitoring and sustainable management of lake and reservoir ecosystems.