An Ensemble-Based Predictive Learning (EBPl) Model for Optimized Water Quality Analysis in Smart Ecosystems

Abstract

Water quality (WQ) is essential to making sure the sustainability of ecosystems, especially in smart environments where automation and data-driven decision-making play key roles. Monitoring and managing water resources efficiently becomes increasingly important as urbanization and industrial activities intensify. Efficient water quality monitoring is crucial for sustainable water utilization in diverse uses, including drinking, bathing, irrigation, and aquaculture. Water quality is evaluated according to its chemical, biological, and physical constituents, with anthropogenic activities such as industrial waste disposal as a significant influencing element. Machine learning (ML) algorithms have recently emerged as efficient methods for WQ classification. This paper focuses on an Ensemble-based predictive learning (EBPL) model for achieving optimized water quality analysis in smart ecosystems by integrating multiple algorithms to provide more accurate, reliable, and adaptable predictions. This work uses a KaggleWQ dataset to train EBPL models that classify WQ according to the Water Quality Index (WQI). The ensemble-based predictive learning (EBPL) model for WQ analysis is developed using Random Forest (RF), AdaBoost, Support Vector Machine (SVM), and hyper-parameter modification using Randomized Search CV. Ensemble learning was proposed to enhance classification accuracy by combining model outputs using voting, stacking, and boosting techniques. This approach leverages the advantages of each model, producing an extremely accurate and reliable system for water quality monitoring in sustainable smart ecosystems.