Water quality fluctuations prediction and Debi estimation based on stochastic optimized weighted ensemble learning machine

Abstract

Today, humanity faces the complex phenomenon of global development. Problems such as limited resources, financial constraints, time constraints, and the involved nature of issues surrounding water resources management pose challenges to effectively monitor water resources issue parameter management (WRIPM). Nevertheless, the importance of this issue cannot be ignored. The focus of this study is on WRIPM issues, and new machine learning models are utilized to investigate and estimate fluctuations in WRIPM parameters. The initial dataset utilized in this study was sourced from the US Geological Survey, specifically sampling stations information from the South Platte River in the United States. Two new models were developed: the Ensemble Bagged Machine (EBM) and the Stochastic Weighted Ensemble Bagged Machine (SWEBM), which further optimized the characteristics of the EBM model using Bayesian Optimization (BN). These models were employed to simulate Dissolved Oxygen (DO), Electrical Conductivity (EC), Power of hydrogen (pH), and river flow rate (Debi) parameters. Additionally, the research employed various scenarios for evaluation and validation. Uncertainties were calculated using the Wilson analysis. The results demonstrated the superiority of the SWEBM in all modeling aspects, yielding the best indices, namely, R2, MAE, and RMSE, at 0.984, 0.0259, and 0.0394, respectively. Performing an receiver operating characteristic analysis revealed that the area over the RROC curve value for the superior model was 36.16, demonstrating excellent performance in modeling the pH parameter. The WM analysis method confirmed that the SWEBM model was the most trustworthy, delivering the greatest precision and best modeling for the pH parameter (WOUB=0.00683, MOPE=0.00138, and STD=0.1665) even though it was slightly overestimated. The probability density function analysis revealed that the SWEBM was the best model. While other models had results that were relatively close. This research represents valuable findings that can be shared with stakeholders, including experts, scholars, and organizations responsible for overseeing water quality administration.