Exploring Groundwater Quality Trends in Valliyar Sub-Basin, Kanniyakumari District, India through Advanced Machine Learning Techniques

Abstract

The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain.