Integrated prediction of water pollution and risk assessment of water system connectivity based on dynamic model average and model selection criteria

• Published in: PloS one Vol. 18; no. 10; p. e0287209
• Main Authors: Ruan, Jinlou, Cui, Yang, Meng, Dechen, Wang, Jifeng, Song, Yuchen, Mao, Yawei
• Format: Journal Article
• Language: English
• Published: San Francisco, CA USA Public Library of Science 19.10.2023
• Subjects: China; Computer and Information Sciences; Earth Sciences; Ecology and Environmental Sciences; Engineering and Technology; Environmental aspects; Management; Physical Sciences; Research and Analysis Methods; Risk assessment; Rivers; Urbanization; Water; Water pollution; Water quality; Water-supply; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0287209

In recent years, with the rapid development of economy and society, river water environmental pollution incidents occur frequently, which seriously threaten the ecological health of the river and the safety of water supply. Water pollution prediction is an important basis for understanding development trends of the aquatic environment, preventing water pollution incidents and improving river water quality. However, due to the large uncertainty of hydrological, meteorological and water environment systems, it is challenging to accurately predict water environment quality using single model. In order to improve the accuracy and stability of water pollution prediction, this study proposed an integrated learning criterion that integrated dynamic model average and model selection (DMA-MS) and used this criterion to construct the integrated learning model for water pollution prediction. Finally, based on the prediction results of the integrated learning model, the connectivity risk of the connectivity project was evaluated. The results demonstrate that the integrated model based on the DMA-MS criterion effectively integrated the characteristics of a single model and could provide more accurate and stable predictions. The mean absolute percentage error (MAPE) of the integrated model was only 11.1%, which was 24.5%-45% lower than that of the single model. In addition, this study indicates that the nearest station was the most important factor affecting the performance of the prediction station, and managers should pay increased attention to the water environment of the control section that is close to their area. The results of the connectivity risk assessment indicate that although the water environment risks were not obvious, the connectivity project may still bring some risks to the crossed water system, especially in the non-flood season.