Remote Sensing and Nonlinear Auto-regressive Neural Network (NARNET) Based Surface Water Chemical Quality Study: A Spatio-Temporal Hybrid Novel Technique (STHNT).

Abstract

In recent days, the quality of water in inland water bodies has been threatened by various natural and anthropogenic activities. Henceforth, the continuous monitoring of water quality is mandatory to control the pollution level in surface water bodies. In this work, remote sensing technology integrated with an Artificial Intelligence (AI) algorithm, a new technique called Spatio-Temporal Hybrid Novel Technique (STHNT), was used to predict, and monitor the chemical water quality pollution level through the Water Quality Index (WQI). The Two Bands Regression Empirical (TBRE) water quality model has been used to retrieve water quality parameters from multi-resolution satellite imagery (Sentinel-2 MSI). The Nonlinear Auto-regressive Neural Network (NARNET), which is an Artificial Neural Network (ANN), was set up to predict the water quality index. Based on the model performed on the remote sensing water quality data, it is inferred that NARNET (Coefficient of determination-R2:0.9911, Root Mean Square Error-RMSE:1.693 and Sum of Squares of Error-SSE:14.33) provides significant results in predicting WQI. Therefore, the combined remote sensing technology with artificial intelligence plays a pivotal role in water resource management, which helps in reducing the pollution level in surface water bodies.