Surface water quality assessment by Random Forest

Abstract

Abstract The energetic nature of these important water resources makes them the most vulnerable to contamination from additional waste from multiple sources. Water quality monitoring is critical to water environmental management, and successful monitoring provides direction and confirms the effectiveness of water management. Models based on artificial intelligence are fundamental for anticipating appropriate moderation measures for surface water quality. In any case, it remains a challenge and requires a requirement to improve display accuracy. Faster and cheaper control is required due to the real-world impact of low water quality. With this inspiration, this research examines an array of machine-learning calculations to estimate water quality. The proposed approach uses Random Forest for modeling and is also useful for predicting surface water quality in the Kulik geographic region of West Bengal, India. It is a good tool for assessing the quality and ensuring the safe use of drinking water. Various water quality parameters (iron, fluoride, total coliform, fecal coliform, pH, total dissolved solids, magnesium, alkalinity, chloride, total hardness, nitrate, calcium, and Escherichia coli) were measured seasonally (winter, summer, rain) over 10 years (2010–2019). The estimated water quality parameters in this study were total dissolved solids (TDS), pH, and iron.