Identifying Urban River Pollution Sources from Wet-Weather Discharges Using an Integrated Deep Learning and Data Assimilation Approach

Identifying Urban River Pollution Sources from Wet-Weather Discharges Using an Integrated Deep Learning and Data Assimilation Approach | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 January 2025 V1 Latest version Share on Identifying Urban River Pollution Sources from Wet-Weather Discharges Using an Integrated Deep Learning and Data Assimilation Approach Authors : Hongzhe Pan , Yiping Li , Jiangjiang Zhang 0000-0002-0513-5233 [email protected] , Chenglong Cao 0009-0004-2920-5330 , Yu Cheng , Yuxuan Zhou , Yaning Wang , Song Bai , Jun Liu , Qiaoyi Jin , and Carlo Gualtieri 0000-0002-3717-1618 Authors Info & Affiliations https://doi.org/10.22541/au.173645585.50247768/v1 Published Journal of Hydrology Version of record Peer review timeline 280 views 179 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Urban rivers often experience significant water quality degradation due to the pollution from wet-weather discharges. Accurate pollution source identification (PSI) is essential for effective river management. However, traditional PSI methods face challenges, including high computational demands and difficulties in addressing equifinality. To overcome these issues, this study introduces an innovative approach that integrates deep learning (DL) with data assimilation (DA). Three DL models-simple Convolutional Neural Networks, ResNet, and UNet-were evaluated as surrogate models for the computationally expensive river water quality model (RWQM). Additionally, three advanced DA methods - DREAM (ZS) , ESMDA, and ILUES - were applied to estimate high-dimensional RWQM parameters. In a numerical case study of a river segment involving 50 unknown parameters across five pollution sources, we assessed the performance of eight approaches for PSI and examined the impacts of monitoring schemes and observation errors. Results showed that UNet provided the highest accuracy in surrogate modeling, while ILUES delivered the best DA performance. The combined UNet-ILUES approach demonstrated a remarkable improvement in computational efficiency, achieving a 406-fold gain compared to the RWQM-ILUES approach. Validated through a real-world water quality degradation event in the Outer Qinhuai River, the UNet-ILUES approach demonstrates strong potential as an efficient solution for characterizing the dynamics of pollution from WWDs in urban rivers, leveraging the combined strengths of DL and DA. Supplementary Material File (1018366_0_merged_1734416828.pdf) Download 7.44 MB File (article_pan.pdf) Download 7.44 MB Information & Authors Information Version history V1 Version 1 09 January 2025 Peer review timeline Published Journal of Hydrology Version of Record 1 Nov 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords data assimilation deep learning pollution source identification urban rivers wet-weather discharge Authors Affiliations Hongzhe Pan Hohai University View all articles by this author Yiping Li Hohai University View all articles by this author Jiangjiang Zhang 0000-0002-0513-5233 [email protected] Hohai University View all articles by this author Chenglong Cao 0009-0004-2920-5330 Hohai University View all articles by this author Yu Cheng Hohai University View all articles by this author Yuxuan Zhou Hohai University View all articles by this author Yaning Wang Hohai University View all articles by this author Song Bai Jiangsu Nanjing Environmental Monitoring Center View all articles by this author Jun Liu Jiangsu Nanjing Environmental Monitoring Center View all articles by this author Qiaoyi Jin Hohai University View all articles by this author Carlo Gualtieri 0000-0002-3717-1618 University of Napoli View all articles by this author Metrics & Citations Metrics Article Usage 280 views 179 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Hongzhe Pan, Yiping Li, Jiangjiang Zhang, et al. Identifying Urban River Pollution Sources from Wet-Weather Discharges Using an Integrated Deep Learning and Data Assimilation Approach. Authorea . 09 January 2025. DOI: https://doi.org/10.22541/au.173645585.50247768/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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