Innovative Water Quality Prediction For Efficient Management Using Ensemble Learning

Abstract

Water quality is of utmost importance for the health and welfare of humans, animals, plants, industries, and the whole ecosystem. Contamination and pollution have had a negative impact on water quality in recent decades. Ensuring the water's purity is vital for the well-being of the population and the long-term preservation of the ecosystem. Conventional approaches to monitoring and predicting water quality often lack precision and promptness. This study investigates ensemble machine learning methods to increase water quality forecast accuracy to address these issues. This method integrates Random Forest, K-Nearest Neighbour, and logistic regression to improve prediction accuracy and durability. The ensemble technique is analyzed using a large dataset of pH, turbidity, dissolved oxygen, and chemical contaminants. The results demonstrate significant improvements in predicting accuracy when compared to individual models, offering a more reliable tool for water quality management. This work showcases the efficacy of ensemble machine learning in producing pragmatic insights for optimizing water management techniques. The system attains an exceptional degree of accuracy, with a rate of 99.98%. Additionally, it demonstrates a high level of precision at 99.87%, recall at 99.67%, F1-score at 99.89%, and Matthews correlation coefficient at 97.86%. These findings have significant implications for improving resource management and protecting the environment.