Effective Monitoring of Noyyal River surface water quality using remote sensing and Machine learning and GIS techniques

Abstract

This study utilizes Geographic Information System (GIS) and remote sensing techniques to predict water quality metrics in the Noyyal River. Satellite data is employed to construct statistical models, with preprocessing involving adjustments from Landsat 8 images. The hybrid LASSO model demonstrates superior performance in predicting water quality parameters, supported by key performance metrics such as RMSE, R-squared, and ANOVA results. The study focuses on assessing the suitability of the hybrid LASSO model for predicting the Water Quality Index (WQI) in the Noyyal region, highlighting its ability to handle high-dimensional data and provide interpretable results. Prediction models employing the LASSO approach yield promising results, with R-squared values exceeding 0.87 for temperature and pH. Incorporating spectral indices significantly enhances model performance, with an average R-squared of 0.8. These models offer cost-effective options for monitoring water quality, revealing poor conditions in the Noyyal River and projecting deterioration for 2023. WQI ratings indicate poor conditions, particularly during July and August 2022 and April 2023, providing valuable insights for local pollution regulation enforcement.