Efficient Water Quality Prediction Using Supervised Machine Learning

TLDR

Water quality is critical for life, yet rapid urbanization and industrialization have degraded it, and traditional lab-based assessments are costly and slow, underscoring the need for faster, cheaper monitoring. The study aims to use supervised machine learning to predict the water quality index and classify water quality based on that index. The approach uses four physical parameters—temperature, turbidity, pH, and total dissolved solids—as inputs to supervised learning models. Gradient boosting and polynomial regression achieved the lowest MAE for WQI (1.96 and 2.73), while an MLP with a (3,7) architecture attained 85% accuracy for WQC, demonstrating that few parameters can yield reliable real‑time water quality predictions.

Abstract

Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.