Optimized Ensemble Model for Predicting Water Toxicity Effects on Aquatic Life using Machine Learning

ABSTRACT

Water toxicity poses a significant threat to aquatic ecosystems and necessitates accurate prediction models to assess potential risks. This paper, proposed an optimized ensemble model that leverages machine learning techniques to predict the toxicity effects of water on aquatic life. Proposed ensemble model aims to improve prediction performance and robustness. We discuss the step-by-step methodology for constructing the ensemble model, including data collection, pre-processing, feature engineering, algorithm selection, model training, ensemble construction, evaluation, and optimization. Additionally, we highlight the importance of interpretability and monitoring in real-world applications. Our results demonstrate that the optimized ensemble model provides enhanced predictive capabilities compared to individual models which is 92.04% Accuracy showcasing its potential for addressing water toxicity challenges.

FULL TEXT