{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T02:16:01Z","timestamp":1779243361745,"version":"3.51.4"},"reference-count":98,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T00:00:00Z","timestamp":1727654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Tibet Autonomous Region","award":["XZ202401ZR0044"],"award-info":[{"award-number":["XZ202401ZR0044"]}]},{"name":"Natural Science Foundation of Tibet Autonomous Region","award":["XZ202301YD0044C"],"award-info":[{"award-number":["XZ202301YD0044C"]}]},{"DOI":"10.13039\/501100018754","name":"Science and Technology Plan Projects of Tibet Autonomous Region","doi-asserted-by":"publisher","award":["XZ202401ZR0044"],"award-info":[{"award-number":["XZ202401ZR0044"]}],"id":[{"id":"10.13039\/501100018754","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018754","name":"Science and Technology Plan Projects of Tibet Autonomous Region","doi-asserted-by":"publisher","award":["XZ202301YD0044C"],"award-info":[{"award-number":["XZ202301YD0044C"]}],"id":[{"id":"10.13039\/501100018754","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accuracy of long-term runoff models can be increased through the input of local weather variables and global climate indices. However, existing methods do not effectively extract important information from complex input factors across various temporal and spatial dimensions, thereby contributing to inaccurate predictions of long-term runoff. In this study, local\u2013global\u2013temporal attention mechanisms (LGTA) were proposed for capturing crucial information on global climate indices on monthly, annual, and interannual time scales. The graph attention network (GAT) was employed to extract geographical topological information of meteorological stations, based on remotely sensed elevation data. A long-term runoff prediction model was established based on long-short-term memory (LSTM) integrated with GAT and LGTA, referred to as GAT\u2013LGTA\u2013LSTM. The proposed model was compared to five comparative models (LGTA\u2013LSTM, GAT\u2013GTA\u2013LSTM, GTA\u2013LSTM, GAT\u2013GA\u2013LSTM, GA\u2013LSTM). The models were applied to forecast the long-term runoff at Luning and Pingshan stations in China. The results indicated that the GAT\u2013LGTA\u2013LSTM model demonstrated the best forecasting performance among the comparative models. The Nash\u2013Sutcliffe Efficiency (NSE) of GAT\u2013LGTA\u2013LSTM at the Luning and Pingshan stations reached 0.87 and 0.89, respectively. Compared to the GA\u2013LSTM benchmark model, the GAT\u2013LGTA\u2013LSTM model demonstrated an average increase in NSE of 0.07, an average increase in Kling\u2013Gupta Efficiency (KGE) of 0.08, and an average reduction in mean absolute percent error (MAPE) of 0.12. The excellent performance of the proposed model is attributed to the following: (1) local attention mechanism assigns a higher weight to key global climate indices at a monthly scale, enhancing the ability of global and temporal attention mechanisms to capture the critical information at annual and interannual scales and (2) the global attention mechanism integrated with GAT effectively extracts crucial temporal and spatial information from precipitation and remotely-sensed elevation data. Furthermore, attention visualization reveals that various global climate indices contribute differently to runoff predictions across distinct months. The global climate indices corresponding to specific seasons or months should be selected to forecast the respective monthly runoff.<\/jats:p>","DOI":"10.3390\/rs16193659","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T11:32:05Z","timestamp":1727695925000},"page":"3659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Local Weather and Global Climate Data-Driven Long-Term Runoff Forecasting Based on Local\u2013Global\u2013Temporal Attention Mechanisms and Graph Attention Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8329-7713","authenticated-orcid":false,"given":"Binlin","family":"Yang","sequence":"first","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5284-5393","authenticated-orcid":false,"given":"Lu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China"},{"name":"School of Water Resources and Civil Engineering, Tibet Agricultural & Animal Husbandry University, Linzhi 860000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9212-1138","authenticated-orcid":false,"given":"Bin","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siming","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiyuan","family":"Leng","sequence":"additional","affiliation":[{"name":"School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China"},{"name":"Hubei Key Laboratory of Digital Valley Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,30]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"A Review of Medium-Long Term Runoff Prediction","volume":"39","author":"Sun","year":"2023","journal-title":"Water Resour. 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