Advanced Remote Sensing and Generative Models for Comprehensive Water Quality Management in a Changing Climate

Water quality is a pivotal factor for maintaining human and ecological health. Traditional water quality assessments often depend on ground sampling and lab tests, which are costly, slow, and constrained by geographic limitations. The emergence of remote sensing technologies now allows for extensive and timely monitoring of water quality across vast regions. This study introduces an innovative approach that utilizes remote sensing data alongside a hybrid Generative Adversarial Network-Long Short-Term Memory (GAN-LSTM) model to transform the monitoring of water quality, focusing on pollution and sanitation management. We employed a comprehensive dataset from Kaggle, which includes 3276 data points and 10 essential water quality indicators, integrated with historical remote sensing data. The GAN model is designed to produce realistic synthetic datasets, which are then used by the LSTM model to predict water quality trends with high accuracy. The methodology achieved notable results, with an accuracy of 98%, precision of 97%, recall of 99%, and an F1-score of 98%. This approach leverages cutting-edge modeling techniques and extensive datasets to significantly improve the monitoring and management of water quality.