{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T02:13:27Z","timestamp":1791425607567,"version":"4.3.3"},"reference-count":41,"publisher":"IWA Publishing","issue":"9","license":[{"start":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T00:00:00Z","timestamp":1714089600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Joint Funds of the Zhejiang Provincial Natural Science Foundation of China","award":["LZJWY24E090001"],"award-info":[{"award-number":["LZJWY24E090001"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3207500"],"award-info":[{"award-number":["2023YFC3207500"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["iwaponline.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n               <jats:p\/>\n               <jats:p>Water quality predicted accuracy is beneficial to river ecological management and water pollution prevention. Owing to water quality data has the characteristics of nonlinearity and instability, it is difficult to predict the change of water quality. This paper proposes a hybrid water quality prediction model based on variational mode decomposition optimized by the sparrow search algorithm (SSA-VMD) and bidirectional gated recursive unit (BiGRU). First, the sparrow search algorithm selects fuzzy entropy (FE) as the fitness function to optimize the two parameters of VMD, which improves the adaptability of VMD. Second, SSA-VMD is used to decompose the original data into several components with different center frequencies. Finally, BiGRU is employed to predict each component separately, which significantly improves predicted accuracy. The proposed model is validated using data about dissolved oxygen (DO) and the potential of hydrogen (pH) from the Xiaojinshan Monitoring Station in Qiandao Lake, Hangzhou, China. The experimental results show that the proposed model has superior prediction accuracy and stability when compared with other models, such as EMD-based models and other CEEMDAN-based models. The prediction accuracy of DO can reach 97.8% and pH is 96.1%. Therefore, the proposed model can provide technical support for river water quality protection and pollution prevention.<\/jats:p>","DOI":"10.2166\/wst.2024.133","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T16:06:15Z","timestamp":1714147575000},"page":"2273-2289","update-policy":"https:\/\/doi.org\/10.2166\/iwapcrossmarkpolicypage","source":"Crossref","is-referenced-by-count":18,"title":["A hybrid water quality prediction model based on variational mode decomposition and bidirectional gated recursive unit"],"prefix":"10.2166","volume":"89","author":[{"given":"Jiange","family":"Jiao","sequence":"first","affiliation":[{"name":"a College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"},{"name":"b Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qianqian","family":"Ma","sequence":"additional","affiliation":[{"name":"a College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senjun","family":"Huang","sequence":"additional","affiliation":[{"name":"c Powerchina Huadong Engineering Corporation Limited, Hangzhou 310014, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanglin","family":"Liu","sequence":"additional","affiliation":[{"name":"a College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanhong","family":"Wan","sequence":"additional","affiliation":[{"name":"d Ocean College, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"945","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"issue":"6","key":"2024051512414178200_WST-D-23-00872C1","doi-asserted-by":"crossref","first-page":"3470","DOI":"10.3390\/su14063470","article-title":"Modeling multistep ahead dissolved oxygen concentration using improved support vector machines by a hybrid metaheuristic algorithm","volume":"14","year":"2022","journal-title":"Sustainability"},{"key":"2024051512414178200_WST-D-23-00872C2","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.marpolbul.2015.06.052","article-title":"Development of wavelet-ANN models to predict water quality parameters in Hilo Bay. 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