{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T01:40:04Z","timestamp":1778895604758,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2019YFC0408601"],"award-info":[{"award-number":["2019YFC0408601"]}]},{"name":"the Key Research and Development Program of Shanxi Province","award":["201903D321052"],"award-info":[{"award-number":["201903D321052"]}]},{"name":"the Natural Science Foundation of Shanxi Province","award":["201901D111060"],"award-info":[{"award-number":["201901D111060"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Water"],"abstract":"<jats:p>Data-intelligent methods designed for forecasting the streamflow of the Fenhe River are crucial for enhancing water resource management. Herein, the gated recurrent unit (GRU) is coupled with the optimization algorithm improved grey wolf optimizer (IGWO) to design a hybrid model (IGWO-GRU) to carry out streamflow forecasting. Two types of predictive structure-based models (sequential IGWO-GRU and monthly IGWO-GRU) are compared with other models, such as the single least-squares support vector machine (LSSVM) and single extreme learning machine (ELM) models. These models incorporate the historical streamflow series as inputs of the model to forecast the future streamflow with data from January 1956 to December 2016 at the Shangjingyou station and from January 1958 to December 2016 at the Fenhe reservoir station. The IGWO-GRU model exhibited a strong ability for mapping in streamflow series when the parameters were carefully tuned. The monthly predictive structure can effectively extract the instinctive hydrological information that is more easily learned by the predictive model than the traditional sequential predictive structure. The monthly IGWO-GRU model was found to be a better forecasting tool, with an average qualification rate of 91.66% in two stations. It also showed good performance in absolute error and peak flow forecasting.<\/jats:p>","DOI":"10.3390\/w13010091","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T08:35:19Z","timestamp":1609749319000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Streamflow Forecasting via Two Types of Predictive Structure-Based Gated Recurrent Unit Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Xuehua","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Water Resources and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanfang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Water Resources and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yizhao","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Water Resources and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shujin","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Data Science and Software Engineering, Baoding University, Baoding 071000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueping","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Water Resources and Engineering, Taiyuan University of Technology, Taiyuan 030024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s11269-006-9070-y","article-title":"Artificial neural network model for synthetic streamflow generation","volume":"21","author":"Ahmed","year":"2007","journal-title":"Water Resour. 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