Estimation of River High Flow Discharges Using Friction-Slope Method and Hybrid Models
Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face un...
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description | Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face uncertainties related to riverbed Manning roughness coefficient and energy slope. This study employs a novel Friction-Slope (α parameter) method to estimate flood discharge. Investigation focuses on three alluvial rivers in Golestan, Iran. The computation method uses the Manning formula and accounts for river energy slope and riverbed Manning roughness coefficient. The α parameter is calculated using easy-to-access river cross-section variables: flow depth, area, and hydraulic radius. SVR-PSO, SVR-GWO, and SVR-RSM hybrid methods are used to achieve this. Calculated river flow discharges are compared to measured data. Statistical evaluation criteria like R
2
, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R
2
value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R
2
= 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98. |
doi_str_mv | 10.1007/s11269-023-03711-w |
format | Article |
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2
, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R
2
value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R
2
= 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98.</description><identifier>ISSN: 0920-4741</identifier><identifier>EISSN: 1573-1650</identifier><identifier>DOI: 10.1007/s11269-023-03711-w</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Alluvial rivers ; Atmospheric Sciences ; Civil Engineering ; Computation ; Constraint modelling ; Discharge ; Earth and Environmental Science ; Earth Sciences ; energy ; Environment ; Estimation ; face ; flood control ; Flood discharge ; Flood forecasting ; Flood management ; Flood predictions ; Floods ; Friction ; Geotechnical Engineering & Applied Earth Sciences ; High flow ; hybrids ; Hydraulic models ; Hydraulic radius ; Hydrogeology ; Hydrologic models ; Hydrology/Water Resources ; Iran ; Mitigation ; Parameters ; prediction ; Reservoir operation ; Resource management ; River beds ; River flow ; River water ; Riverbeds ; Rivers ; Roughness ; Roughness coefficient ; Slope ; stream channels ; Stream flow ; Water depth ; Water flow ; Water resources ; Water resources management</subject><ispartof>Water resources management, 2024-02, Vol.38 (3), p.1099-1123</ispartof><rights>The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c303t-3aa1c5650b57fd2cb9630fbdd6afc5bdaff41a924c234ac474e0fb71ee38ba023</cites><orcidid>0000-0003-2009-5046</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11269-023-03711-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11269-023-03711-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids></links><search><creatorcontrib>Shirazi, Fatemeh</creatorcontrib><creatorcontrib>Zahiri, Abdolreza</creatorcontrib><creatorcontrib>Piri, Jamshid</creatorcontrib><creatorcontrib>Dehghani, Amir Ahmad</creatorcontrib><title>Estimation of River High Flow Discharges Using Friction-Slope Method and Hybrid Models</title><title>Water resources management</title><addtitle>Water Resour Manage</addtitle><description>Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face uncertainties related to riverbed Manning roughness coefficient and energy slope. This study employs a novel Friction-Slope (α parameter) method to estimate flood discharge. Investigation focuses on three alluvial rivers in Golestan, Iran. The computation method uses the Manning formula and accounts for river energy slope and riverbed Manning roughness coefficient. The α parameter is calculated using easy-to-access river cross-section variables: flow depth, area, and hydraulic radius. SVR-PSO, SVR-GWO, and SVR-RSM hybrid methods are used to achieve this. Calculated river flow discharges are compared to measured data. Statistical evaluation criteria like R
2
, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R
2
value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R
2
= 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98.</description><subject>Alluvial rivers</subject><subject>Atmospheric Sciences</subject><subject>Civil Engineering</subject><subject>Computation</subject><subject>Constraint modelling</subject><subject>Discharge</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>energy</subject><subject>Environment</subject><subject>Estimation</subject><subject>face</subject><subject>flood control</subject><subject>Flood discharge</subject><subject>Flood forecasting</subject><subject>Flood management</subject><subject>Flood predictions</subject><subject>Floods</subject><subject>Friction</subject><subject>Geotechnical Engineering & Applied Earth Sciences</subject><subject>High flow</subject><subject>hybrids</subject><subject>Hydraulic models</subject><subject>Hydraulic radius</subject><subject>Hydrogeology</subject><subject>Hydrologic models</subject><subject>Hydrology/Water Resources</subject><subject>Iran</subject><subject>Mitigation</subject><subject>Parameters</subject><subject>prediction</subject><subject>Reservoir operation</subject><subject>Resource management</subject><subject>River beds</subject><subject>River flow</subject><subject>River water</subject><subject>Riverbeds</subject><subject>Rivers</subject><subject>Roughness</subject><subject>Roughness coefficient</subject><subject>Slope</subject><subject>stream channels</subject><subject>Stream flow</subject><subject>Water depth</subject><subject>Water flow</subject><subject>Water resources</subject><subject>Water resources management</subject><issn>0920-4741</issn><issn>1573-1650</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp9kMtKAzEUhoMoWKsv4Crgxk00l7k0S6mtFSyCWrchk8s0ZTqpydTStzd1BMGFq7PI95_z5wPgkuAbgnF5GwmhBUeYMoRZSQjaHYEByUuGSJHjYzDAnGKUlRk5BWcxrjBOMY4H4H0SO7eWnfMt9Ba-uE8T4MzVSzht_A7eu6iWMtQmwkV0bQ2nwakDjF4bvzFwbrql11C2Gs72VXAazr02TTwHJ1Y20Vz8zCFYTCdv4xl6en54HN89IcUw6xCTkqg8Nazy0mqqKl4wbCutC2lVXmlpbUYkp5miLJMq9TfpuSTGsFEl02-H4Lrfuwn-Y2tiJ9apsWka2Rq_jYKRnOW8xCOe0Ks_6MpvQ5vaCcpZskE4ZYmiPaWCjzEYKzYh-Ql7QbA4qBa9apGOi2_VYpdCrA_FBLe1Cb-r_0l9AXs1gcQ</recordid><startdate>20240201</startdate><enddate>20240201</enddate><creator>Shirazi, Fatemeh</creator><creator>Zahiri, Abdolreza</creator><creator>Piri, Jamshid</creator><creator>Dehghani, Amir Ahmad</creator><general>Springer Netherlands</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H97</scope><scope>KR7</scope><scope>L.G</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-2009-5046</orcidid></search><sort><creationdate>20240201</creationdate><title>Estimation of River High Flow Discharges Using Friction-Slope Method and Hybrid Models</title><author>Shirazi, Fatemeh ; Zahiri, Abdolreza ; Piri, Jamshid ; Dehghani, Amir Ahmad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c303t-3aa1c5650b57fd2cb9630fbdd6afc5bdaff41a924c234ac474e0fb71ee38ba023</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Alluvial rivers</topic><topic>Atmospheric Sciences</topic><topic>Civil Engineering</topic><topic>Computation</topic><topic>Constraint modelling</topic><topic>Discharge</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>energy</topic><topic>Environment</topic><topic>Estimation</topic><topic>face</topic><topic>flood control</topic><topic>Flood discharge</topic><topic>Flood forecasting</topic><topic>Flood management</topic><topic>Flood predictions</topic><topic>Floods</topic><topic>Friction</topic><topic>Geotechnical Engineering & Applied Earth Sciences</topic><topic>High flow</topic><topic>hybrids</topic><topic>Hydraulic models</topic><topic>Hydraulic radius</topic><topic>Hydrogeology</topic><topic>Hydrologic models</topic><topic>Hydrology/Water Resources</topic><topic>Iran</topic><topic>Mitigation</topic><topic>Parameters</topic><topic>prediction</topic><topic>Reservoir operation</topic><topic>Resource management</topic><topic>River beds</topic><topic>River flow</topic><topic>River water</topic><topic>Riverbeds</topic><topic>Rivers</topic><topic>Roughness</topic><topic>Roughness coefficient</topic><topic>Slope</topic><topic>stream channels</topic><topic>Stream flow</topic><topic>Water depth</topic><topic>Water flow</topic><topic>Water resources</topic><topic>Water resources management</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shirazi, Fatemeh</creatorcontrib><creatorcontrib>Zahiri, Abdolreza</creatorcontrib><creatorcontrib>Piri, Jamshid</creatorcontrib><creatorcontrib>Dehghani, Amir Ahmad</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Water resources management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shirazi, Fatemeh</au><au>Zahiri, Abdolreza</au><au>Piri, Jamshid</au><au>Dehghani, Amir Ahmad</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation of River High Flow Discharges Using Friction-Slope Method and Hybrid Models</atitle><jtitle>Water resources management</jtitle><stitle>Water Resour Manage</stitle><date>2024-02-01</date><risdate>2024</risdate><volume>38</volume><issue>3</issue><spage>1099</spage><epage>1123</epage><pages>1099-1123</pages><issn>0920-4741</issn><eissn>1573-1650</eissn><abstract>Accurately estimating river water flow during floods is crucial to water resource management, dam reservoir operation, and flood mitigation strategies. Although hydrological models for flood prediction have improved, they still face constraints and make inaccurate forecasts. Hydraulic models face uncertainties related to riverbed Manning roughness coefficient and energy slope. This study employs a novel Friction-Slope (α parameter) method to estimate flood discharge. Investigation focuses on three alluvial rivers in Golestan, Iran. The computation method uses the Manning formula and accounts for river energy slope and riverbed Manning roughness coefficient. The α parameter is calculated using easy-to-access river cross-section variables: flow depth, area, and hydraulic radius. SVR-PSO, SVR-GWO, and SVR-RSM hybrid methods are used to achieve this. Calculated river flow discharges are compared to measured data. Statistical evaluation criteria like R
2
, MAE, RMSE, and conformity index determined the hybrid models' optimal structures. The SVR-RSM model had the highest accuracy during testing, with an R
2
value of 0.97, MAE of 0.22, RMSE of 1.66, and d of 0.99. Once the α parameter was determined using the RSM-SVR model, river flow discharges were calculated and compared to observed values. The testing phase produced the most accurate results, with R
2
= 0.88, MAE = 0.15, RMSE = 0.41, and d = 0.98.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11269-023-03711-w</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0003-2009-5046</orcidid></addata></record> |
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subjects | Alluvial rivers Atmospheric Sciences Civil Engineering Computation Constraint modelling Discharge Earth and Environmental Science Earth Sciences energy Environment Estimation face flood control Flood discharge Flood forecasting Flood management Flood predictions Floods Friction Geotechnical Engineering & Applied Earth Sciences High flow hybrids Hydraulic models Hydraulic radius Hydrogeology Hydrologic models Hydrology/Water Resources Iran Mitigation Parameters prediction Reservoir operation Resource management River beds River flow River water Riverbeds Rivers Roughness Roughness coefficient Slope stream channels Stream flow Water depth Water flow Water resources Water resources management |
title | Estimation of River High Flow Discharges Using Friction-Slope Method and Hybrid Models |
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