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<p>Environmental monitoring and evidence-based decision-making in water resources require robust water-quality (WQ) modeling frameworks. Dissolved oxygen (DO) and pH are key indicators addressed in this study using a data-driven approach. The South Platte River Basin, United States, is selected as the case study, with data obtained from the United States Geological Survey (USGS) and preprocessed prior to modeling. This study proposes a dynamically optimized weighted ensemble deep learning (DL-EDL) framework, in which ensemble weights are adaptively determined through nonlinear programming rather than fixed or heuristically assigned schemes. The proposed model achieves R² values of 0.72 for DO and 0.82 for pH, indicating satisfactory predictive performance for complex environmental time-series data. In addition, uncertainty analysis based on bootstrap and Monte Carlo uncertainty analyses shows that the DL-EDL model yields lower prediction variability, with reduced standard deviations, 0.035 for DO and 0.004 for pH in bootstrap analysis, confirming the stability and reliability of the model outputs. Overall, the proposed framework provides a practical tool for water-quality monitoring and decision support in water resource management.</p>

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A New Explainable Modeling of Water Quality by Deep Learning Approaches: Application to Dissolved Oxygen and pH Prediction

  • 期刊论文

  • 发表日期：

    2026年5月19日

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  • Mojtaba Poursaeid

摘要

Environmental monitoring and evidence-based decision-making in water resources require robust water-quality (WQ) modeling frameworks. Dissolved oxygen (DO) and pH are key indicators addressed in this study using a data-driven approach. The South Platte River Basin, United States, is selected as the case study, with data obtained from the United States Geological Survey (USGS) and preprocessed prior to modeling. This study proposes a dynamically optimized weighted ensemble deep learning (DL-EDL) framework, in which ensemble weights are adaptively determined through nonlinear programming rather than fixed or heuristically assigned schemes. The proposed model achieves R² values of 0.72 for DO and 0.82 for pH, indicating satisfactory predictive performance for complex environmental time-series data. In addition, uncertainty analysis based on bootstrap and Monte Carlo uncertainty analyses shows that the DL-EDL model yields lower prediction variability, with reduced standard deviations, 0.035 for DO and 0.004 for pH in bootstrap analysis, confirming the stability and reliability of the model outputs. Overall, the proposed framework provides a practical tool for water-quality monitoring and decision support in water resource management.