Improved runoff forecasting based on time-varying model averaging method and deep learning
In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables...
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| Veröffentlicht in: | PloS one 2022-09, Vol.17 (9), p.e0274004 |
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| creator | Ran, Jinlou Cui, Yang Xiang, Kai Song, Yuchen |
| description | In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction. |
| doi_str_mv | 10.1371/journal.pone.0274004 |
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This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0274004</identifier><identifier>PMID: 36108081</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accuracy ; Algorithms ; Analysis ; Biology and Life Sciences ; Computer and Information Sciences ; Correlation analysis ; Deep Learning ; Dynamic models ; Dynamic stability ; Earth Sciences ; Evaluation ; Flood predictions ; Floods ; Forecasting ; Forecasts and trends ; Hydrologic cycle ; Hydrology ; Machine learning ; Methods ; Modelling ; Monthly rainfall ; Monthly runoff ; Physical Sciences ; Prediction models ; Probability ; Rain ; Rainfall ; Research and Analysis Methods ; Runoff ; Runoff forecasting ; Simulation ; Time series ; Uncertainty ; Water Movements ; Weather forecasting</subject><ispartof>PloS one, 2022-09, Vol.17 (9), p.e0274004</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Ran et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Ran et al 2022 Ran et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c585t-60cdc21ad89ec8b1a10e814c3831fdd98e5d90a6b0c2fb1c0141ef6504a2a0943</citedby><cites>FETCH-LOGICAL-c585t-60cdc21ad89ec8b1a10e814c3831fdd98e5d90a6b0c2fb1c0141ef6504a2a0943</cites><orcidid>0000-0002-9570-3985</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477370/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9477370/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36108081$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ran, Jinlou</creatorcontrib><creatorcontrib>Cui, Yang</creatorcontrib><creatorcontrib>Xiang, Kai</creatorcontrib><creatorcontrib>Song, Yuchen</creatorcontrib><title>Improved runoff forecasting based on time-varying model averaging method and deep learning</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Analysis</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Correlation analysis</subject><subject>Deep Learning</subject><subject>Dynamic models</subject><subject>Dynamic stability</subject><subject>Earth Sciences</subject><subject>Evaluation</subject><subject>Flood predictions</subject><subject>Floods</subject><subject>Forecasting</subject><subject>Forecasts and trends</subject><subject>Hydrologic cycle</subject><subject>Hydrology</subject><subject>Machine learning</subject><subject>Methods</subject><subject>Modelling</subject><subject>Monthly rainfall</subject><subject>Monthly runoff</subject><subject>Physical 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based on time-varying model averaging method and deep learning</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2022-09-15</date><risdate>2022</risdate><volume>17</volume><issue>9</issue><spage>e0274004</spage><pages>e0274004-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>In order to improve the accuracy and stability of runoff prediction. This study proposed a dynamic model averaging method with Time-varying weight (TV-DMA). Using this method, an integrated prediction model framework for runoff prediction was constructed. The framework determines the main variables suitable for runoff prediction through correlation analysis, and uses TV-DMA and deep learning algorithm to construct an integrated prediction model for runoff. The results demonstrate that the current monthly runoff, the runoff of the previous month, the current monthly temperature, the temperature of the previous month and the current monthly rainfall were the variables suitable for runoff prediction. The results of runoff prediction show that the TV-DMA model has the highest prediction accuracy (with 0.97 Nash-efficiency coefficient (NSE)) and low uncertainty. The interval band of uncertainty was 33.3%-65.5% lower than single model. And the prediction performance of the single model and TV-DMA model in flood season is obviously lower than that in non-flood season. In addition, this study indicate that the current monthly runoff, rainfall and temperature are the important factor affecting the runoff prediction, which should be paid special attention in the runoff prediction.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36108081</pmid><doi>10.1371/journal.pone.0274004</doi><orcidid>https://orcid.org/0000-0002-9570-3985</orcidid><oa>free_for_read</oa></addata></record> |
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| source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accuracy Algorithms Analysis Biology and Life Sciences Computer and Information Sciences Correlation analysis Deep Learning Dynamic models Dynamic stability Earth Sciences Evaluation Flood predictions Floods Forecasting Forecasts and trends Hydrologic cycle Hydrology Machine learning Methods Modelling Monthly rainfall Monthly runoff Physical Sciences Prediction models Probability Rain Rainfall Research and Analysis Methods Runoff Runoff forecasting Simulation Time series Uncertainty Water Movements Weather forecasting |
| title | Improved runoff forecasting based on time-varying model averaging method and deep learning |
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