Performance comparison of various machine learning models for predicting water quality parameters in the Chebika Zone of Central Tunisia

Abstract

This groundbreaking study pioneers the application of state-of-the-art machine learning algorithms to predict pivotal water parameters, specifically pH, water depth, and salinity. Rigorously evaluating four leading algorithms (Random Forest Regressor, MLP Regressor, Support Vector Machine, and XGB Regressor) leveraging a substantial dataset and employing comprehensive metrics, including R², MSE, MAE, and cross-validation scores.

Results unequivocally demonstrate the exceptional performance of MLP Regressor and XGB Regressor, consistently outclassing other models in predicting pH, with remarkable R² values and minimal errors. MLP Regressor excels as the preeminent model for water depth prediction, while XGB Regressor leads in accurately predicting salinity. The study underscores the paramount importance of cross-validation in meticulously assessing model robustness and generalization capabilities.

A distinctive feature of this research lies in its innovative approach, incorporating geographic localization data (longitude, latitude, and altitude) as exclusive inputs for all models. This strategic integration showcases the algorithms' unprecedented ability to predict water parameters based solely on geographical coordinates, underscoring the transformative potential of machine learning in revolutionizing water resource management.

The implications extend far beyond its immediate focus, encompassing critical areas such as geophysical exploration, environmental monitoring, water quality management, and ecological conservation. By providing invaluable insights into the application of machine learning algorithms for predicting key water parameters, this study positions itself at the forefront of scientific contributions, setting a new standard for excellence in sustainable water resource utilization.