Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms
Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow predi...
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| Veröffentlicht in: | Hydrological sciences journal 2021-11, Vol.66 (15), p.2155-2169 |
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| container_title | Hydrological sciences journal |
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| creator | Dodangeh, Esmaeel Ewees, Ahmed A. Shahid, Shamsuddin Yaseen, Zaher Mundher |
| description | Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE ≈ 3.75 m
3
.s
−1
) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall-runoff relationships. |
| doi_str_mv | 10.1080/02626667.2021.1985123 |
| format | Article |
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3
.s
−1
) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall-runoff relationships.</description><identifier>ISSN: 0262-6667</identifier><identifier>EISSN: 2150-3435</identifier><identifier>DOI: 10.1080/02626667.2021.1985123</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>Adaptive systems ; Algorithms ; ANFIS ; Artificial neural networks ; catchment management ; Daily ; Daily rainfall ; Drinking water ; Flow simulation ; Fuzzy logic ; Heuristic methods ; hybrid model ; Hybridization ; Intelligence ; metaheuristic algorithms ; Modelling ; Optimization ; Performance prediction ; Predictions ; Rain ; Rainfall ; Rainfall runoff ; Rainfall-runoff relationships ; River flow ; river flow prediction ; Rivers ; Root-mean-square errors ; Runoff ; Simulation ; Stream flow ; Swarm intelligence</subject><ispartof>Hydrological sciences journal, 2021-11, Vol.66 (15), p.2155-2169</ispartof><rights>2021 IAHS 2021</rights><rights>2021 IAHS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-743092b2c62fd28aa16a5125ba126f729aec2d7d12646b3f14c8781bd30061fa3</citedby><cites>FETCH-LOGICAL-c338t-743092b2c62fd28aa16a5125ba126f729aec2d7d12646b3f14c8781bd30061fa3</cites><orcidid>0000-0001-9621-6452 ; 0000-0003-3647-7137</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/02626667.2021.1985123$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/02626667.2021.1985123$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,59647,60436</link.rule.ids></links><search><creatorcontrib>Dodangeh, Esmaeel</creatorcontrib><creatorcontrib>Ewees, Ahmed A.</creatorcontrib><creatorcontrib>Shahid, Shamsuddin</creatorcontrib><creatorcontrib>Yaseen, Zaher Mundher</creatorcontrib><title>Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms</title><title>Hydrological sciences journal</title><description>Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE ≈ 3.75 m
3
.s
−1
) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall-runoff relationships.</description><subject>Adaptive systems</subject><subject>Algorithms</subject><subject>ANFIS</subject><subject>Artificial neural networks</subject><subject>catchment management</subject><subject>Daily</subject><subject>Daily rainfall</subject><subject>Drinking water</subject><subject>Flow simulation</subject><subject>Fuzzy logic</subject><subject>Heuristic methods</subject><subject>hybrid model</subject><subject>Hybridization</subject><subject>Intelligence</subject><subject>metaheuristic algorithms</subject><subject>Modelling</subject><subject>Optimization</subject><subject>Performance prediction</subject><subject>Predictions</subject><subject>Rain</subject><subject>Rainfall</subject><subject>Rainfall runoff</subject><subject>Rainfall-runoff relationships</subject><subject>River flow</subject><subject>river flow prediction</subject><subject>Rivers</subject><subject>Root-mean-square errors</subject><subject>Runoff</subject><subject>Simulation</subject><subject>Stream flow</subject><subject>Swarm intelligence</subject><issn>0262-6667</issn><issn>2150-3435</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp9kMtOwzAQRS0EEqXwCUiWWKf4kTgJK1B5SpXYwBJZk9huXTlxsROq9OtJ1bJlNZqZc-9oLkLXlMwoKcgtYYIJIfIZI4zOaFlklPETNGE0IwlPeXaKJnsm2UPn6CLGNSE8LQWfoK9HsG7AsQancbA_OmDj_BZH2_QOOuvbO7waqmCV3WmFTb_bDdj5pa1x45V2eGu7FW50ByvdBxu7cQFu6cM4buIlOjPgor461in6fH76mL8mi_eXt_nDIqk5L7okTzkpWcVqwYxiBQAVMP6QVUCZMDkrQddM5WrsUlFxQ9O6yAtaKU6IoAb4FN0cfDfBf_c6dnLt-9COJyUTJEtZmTI6UtmBqoOPMWgjN8E2EAZJidwnKf-SlPsk5THJUXd_0NnW-NDA1genZAeD88EEaGsbJf_f4hfyH3sQ</recordid><startdate>20211118</startdate><enddate>20211118</enddate><creator>Dodangeh, Esmaeel</creator><creator>Ewees, Ahmed A.</creator><creator>Shahid, Shamsuddin</creator><creator>Yaseen, Zaher Mundher</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0001-9621-6452</orcidid><orcidid>https://orcid.org/0000-0003-3647-7137</orcidid></search><sort><creationdate>20211118</creationdate><title>Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms</title><author>Dodangeh, Esmaeel ; Ewees, Ahmed A. ; Shahid, Shamsuddin ; Yaseen, Zaher Mundher</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-743092b2c62fd28aa16a5125ba126f729aec2d7d12646b3f14c8781bd30061fa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adaptive systems</topic><topic>Algorithms</topic><topic>ANFIS</topic><topic>Artificial neural networks</topic><topic>catchment management</topic><topic>Daily</topic><topic>Daily rainfall</topic><topic>Drinking water</topic><topic>Flow simulation</topic><topic>Fuzzy logic</topic><topic>Heuristic methods</topic><topic>hybrid model</topic><topic>Hybridization</topic><topic>Intelligence</topic><topic>metaheuristic algorithms</topic><topic>Modelling</topic><topic>Optimization</topic><topic>Performance prediction</topic><topic>Predictions</topic><topic>Rain</topic><topic>Rainfall</topic><topic>Rainfall runoff</topic><topic>Rainfall-runoff relationships</topic><topic>River flow</topic><topic>river flow prediction</topic><topic>Rivers</topic><topic>Root-mean-square errors</topic><topic>Runoff</topic><topic>Simulation</topic><topic>Stream flow</topic><topic>Swarm intelligence</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dodangeh, Esmaeel</creatorcontrib><creatorcontrib>Ewees, Ahmed A.</creatorcontrib><creatorcontrib>Shahid, Shamsuddin</creatorcontrib><creatorcontrib>Yaseen, Zaher Mundher</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><jtitle>Hydrological sciences journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dodangeh, Esmaeel</au><au>Ewees, Ahmed A.</au><au>Shahid, Shamsuddin</au><au>Yaseen, Zaher Mundher</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms</atitle><jtitle>Hydrological sciences journal</jtitle><date>2021-11-18</date><risdate>2021</risdate><volume>66</volume><issue>15</issue><spage>2155</spage><epage>2169</epage><pages>2155-2169</pages><issn>0262-6667</issn><eissn>2150-3435</eissn><abstract>Novel data-intelligence models developed through hybridization of an adaptive neuro-fuzzy inference system (ANFIS) with different metaheuristic algorithms, namely grey wolf optimizer (GWO), particle swarm optimizer (PSO) and whale optimization algorithm (WOA), are proposed for daily river flow prediction of the Taleghan River, which is the major source of potable water for Tehran, the capital of Iran. Gamma test (GT) was used for the determination of input variables for the models. The ANFIS-WOA model was found to exhibit the best performance in prediction of river flow according to root mean square error (RMSE ≈ 3.75 m
3
.s
−1
) and Nash-Sutcliffe efficiency (NSE ≈ 0.93). It improved the prediction performance of the classical ANFIS model by 6.5%. The convergence speed of ANFIS-WOA was also found to be higher compared to other hybrid models. The success of the ANFIS-WOA model indicates its potential for use in the simulation of highly nonlinear daily rainfall-runoff relationships.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/02626667.2021.1985123</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0001-9621-6452</orcidid><orcidid>https://orcid.org/0000-0003-3647-7137</orcidid></addata></record> |
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| subjects | Adaptive systems Algorithms ANFIS Artificial neural networks catchment management Daily Daily rainfall Drinking water Flow simulation Fuzzy logic Heuristic methods hybrid model Hybridization Intelligence metaheuristic algorithms Modelling Optimization Performance prediction Predictions Rain Rainfall Rainfall runoff Rainfall-runoff relationships River flow river flow prediction Rivers Root-mean-square errors Runoff Simulation Stream flow Swarm intelligence |
| title | Daily scale river flow simulation: hybridized fuzzy logic model with metaheuristic algorithms |
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