A HYBRID MACHINE LEARNING APPROACH FOR ENHANCED WATER QUALITY PREDICTION: INTEGRATING RANDOM FOREST AND GRADIENT BOOSTING
DOI:
https://doi.org/10.35631/IJIREV.825014Keywords:
Gradient Boosting, Machine Learning Fusion, Random Forest, Water Quality PredictionAbstract
The escalating contamination of global water resources poses significant challenges to human health and environmental sustainability, necessitating the development of rapid, high-precision monitoring technologies. While traditional chemical and biological assessments are reliable, they are often hindered by high costs, complexity, and significant analytical latency. In response to the Industry 4.0 paradigm, this research proposes a hybrid machine learning framework that integrates Random Forest (RF) and Gradient Boosting (GB) through two fusion strategies: error-based boosting and weighted model averaging to automate and optimise water quality prediction. It implements a structured methodology including data preprocessing, feature extraction, and hyperparameter tuning. By leveraging the complementary strengths of RF (overfitting resistance) and GB (sequential error reduction), the fusion model is designed to capture nonlinear relationships in the datasets. Experimental results demonstrate that the optimised hybrid approach outperforms standalone models, achieving higher prediction accuracy and better generalisation. Furthermore, it provides a comparative analysis of feature configurations, identifying the optimal parameters for reliable detection. The findings suggest that integrating such intelligent algorithms into environmental management systems can improve automation and decision-making in water monitoring. The study concludes by outlining future trajectories, emphasising the integration of Deep Learning and Big Data analytics to further refine the practicality of water quality assessment. Ultimately, it provides a basis for further development and application for sustainable water resource management in the era of digital transformation.
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