GIS-Based Modelling for Estimation of Water Quality Parameters: A Review

This chapter offers a comprehensive review of geographic information system (GIS)-based approaches for estimating water quality parameters. It highlights the advantages of using GIS such as integrating satellite imagery and spatial data and conducting spatial analysis. The chapter emphasizes the significance of water quality monitoring and the limitations of traditional analysis methods. It explores various types of GIS-based models, including empirical, process-based, and hybrid models. Additionally, it suggests the use of remote sensing and machine learning techniques, such as deep learning, for more accurate and timely water quality forecasting. The chapter covers the estimation of both optically active and inactive parameters through remote sensing. It summarizes previous studies utilizing GIS-based approaches, including machine learning, for water quality estimation. The limitations and challenges, such as uncertainty and validation, are discussed, along with recommendations for future research. The chapter highlights the potential of GIS-based modelling in improving water quality management and stresses the importance of interdisciplinary collaboration.