Optimized Water Quality Forecasting Using Machine Learning

Water is one of most critical assets for a healthy life, covering around 70% of the Earth's surface. However, rapid industrialization and urbanization have led to an alarming rate of water quality degradation, resulting in horrible diseases. Traditional techniques for evaluating water quality are expensive, time-consuming, and require laboratory and statistical tests, making real-time monitoring impractical. Therefore, there is a need for a quicker and more workable solution to address the catastrophic consequences of poor water quality. To this end, a particular index called Water Quality Index (WQI) is defined to complete Water Quality Class (WQC) and Water Quality (WQ) and use the WQI. So, in this research work, we explore several supervised machine learning methods to calculate WQI. Suggested approach uses four input parameters: Total Dissolved Solids (TDS), pH, turbidity, and temperature. The most effective algorithms for forecasting the WQI are gradient boosting and polynomial regression with degree of 2 with learning rate of 0.1. However, these algorithms have Mean Absolute Errors (MAE) of 1.8074 and 2.8957. Conversely, a Multi-Layer Perceptron (MLP) classifies WQC most accurately, with accuracy of 0.8796 and a configuration of (3, 7). Proposed approach confirms it’s prospective for use in water quality detecting systems by attaining rational accuracy with inadequate set of parameters. Therefore, it is crucial to develop more accurate methods to monitor and cope with WQ to ensure the sustainability of life on Earth.