Comprehensive water quality indicators modeling by environmental protection view using multi optimized weighted ensemble machine learnings

• Published in: Process safety and environmental protection Vol. 193; pp. 696 - 709
• Main Author: Poursaeid, Mojtaba
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2025
• Subjects: Ensemble machine learning; Linear programming; Nelder Mead; Nonlinear programming; Simulating annealing; Water quality; Ensemble machine learning; Linear programming; Nonlinear programming; Nelder Mead; Water quality; Simulating annealing
• ISSN: 0957-5820
• DOI: 10.1016/j.psep.2024.11.042

The environmental protection water quality is a critical subject in dominance of the sustainable water resources management. Accordingly, the essential indicators of water quality were considered, which are not only appropriate indicators for water quality, but they can also serve as crucial indicators for the health of water environment and ecosystems. Therefore, this study uses well-known ensemble machine learning methodologies to investigate and predict the fluctuations of water quality parameters. Optimization procedures used to assemble machine learnings were non-linear programming (NLP), genetic, gradient descent, and least square algorithms, linear programming, particle swarm optimization, Nelder-Mead optimization, and simulating annealing optimization. Using optimization procedures, the basic MLs were assembled and eight new ensemble machine learning were developed. The studied area was the South Platte River basin, USA. The primary dataset was obtained through the online database of the United States Geological Survey, which contained sampling information on river water related to 2023–2024. Then, using clean missing and outlier data preprocessing techniques, the dataset was modified. Finally, using the 10-fold cross-validation technique, the primary data was validated. The results showed that NLP significantly improved the accuracy and performance of models, achieving the best performance with R2 of 0.9836 and 0.9031 across DO and pH modelings. The modeling results indicated that the pH parameter fluctuated within the safe range. While the DO seems that tolerated in unsafe domain for aquatic ecosystems. The findings of this research could help a wide range of decision-makers.