A hybrid method for forecasting river-suspended sediments in Iran
Estimation of sediment mass carried by rivers is an important issue in Hydrological Sciences. The main purpose of this research was to find an appropriate method to compute sediment discharge. Some machine-learning approaches have been used to forecast river-suspended sediments, correctly. One of th...
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| Veröffentlicht in: | International journal of river basin management 2017-10, Vol.15 (4), p.453-460 |
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| creator | Tavakoli Targhi, Alireza Abbaszadeh, Sadegh Arabasadi, Zeinab |
| description | Estimation of sediment mass carried by rivers is an important issue in Hydrological Sciences. The main purpose of this research was to find an appropriate method to compute sediment discharge. Some machine-learning approaches have been used to forecast river-suspended sediments, correctly. One of the most effective and traditional approaches for forecasting events is to use artificial neural networks (ANNs). So, we are going to improve the performance of ANNs in estimation of suspended sediments, upon a data of Baba Aman basin in Iran. We first apply a typical neural network and obtain the root-mean-square-error of
and the correlation coefficient of
. Then, to improve the prediction ability of ANNs, we hybridize this method with cuckoo optimization algorithm (COA). Combination of ANNs with COA causes reduction in root-mean-square-error to
, increasing in correlation coefficient to
and also proposing a better model. |
| doi_str_mv | 10.1080/15715124.2017.1315815 |
| format | Article |
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and the correlation coefficient of
. Then, to improve the prediction ability of ANNs, we hybridize this method with cuckoo optimization algorithm (COA). Combination of ANNs with COA causes reduction in root-mean-square-error to
, increasing in correlation coefficient to
and also proposing a better model.</description><identifier>ISSN: 1571-5124</identifier><identifier>EISSN: 1814-2060</identifier><identifier>DOI: 10.1080/15715124.2017.1315815</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>artificial neural network ; Artificial neural networks ; Correlation coefficient ; Correlation coefficients ; cuckoo optimization algorithm ; discharge ; Fluvial sediments ; Forecasting ; Hydrology ; Learning algorithms ; Machine learning ; Mathematical models ; Methods ; Neural networks ; River forecasting ; Rivers ; Sediment ; Sediment discharge ; Sediments ; Suspended sediments</subject><ispartof>International journal of river basin management, 2017-10, Vol.15 (4), p.453-460</ispartof><rights>2017 International Association for Hydro-Environment Engineering and Research 2017</rights><rights>2017 International Association for Hydro-Environment Engineering and Research</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-edd14374f9b4ac10a8aafd255b19582f94305c13ca948840769274946ebb0c0a3</citedby><cites>FETCH-LOGICAL-c338t-edd14374f9b4ac10a8aafd255b19582f94305c13ca948840769274946ebb0c0a3</cites><orcidid>0000-0002-8257-0116</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/15715124.2017.1315815$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/15715124.2017.1315815$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,59620,60409</link.rule.ids></links><search><creatorcontrib>Tavakoli Targhi, Alireza</creatorcontrib><creatorcontrib>Abbaszadeh, Sadegh</creatorcontrib><creatorcontrib>Arabasadi, Zeinab</creatorcontrib><title>A hybrid method for forecasting river-suspended sediments in Iran</title><title>International journal of river basin management</title><description>Estimation of sediment mass carried by rivers is an important issue in Hydrological Sciences. The main purpose of this research was to find an appropriate method to compute sediment discharge. Some machine-learning approaches have been used to forecast river-suspended sediments, correctly. One of the most effective and traditional approaches for forecasting events is to use artificial neural networks (ANNs). So, we are going to improve the performance of ANNs in estimation of suspended sediments, upon a data of Baba Aman basin in Iran. We first apply a typical neural network and obtain the root-mean-square-error of
and the correlation coefficient of
. Then, to improve the prediction ability of ANNs, we hybridize this method with cuckoo optimization algorithm (COA). Combination of ANNs with COA causes reduction in root-mean-square-error to
, increasing in correlation coefficient to
and also proposing a better model.</description><subject>artificial neural network</subject><subject>Artificial neural networks</subject><subject>Correlation coefficient</subject><subject>Correlation coefficients</subject><subject>cuckoo optimization algorithm</subject><subject>discharge</subject><subject>Fluvial sediments</subject><subject>Forecasting</subject><subject>Hydrology</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Mathematical models</subject><subject>Methods</subject><subject>Neural networks</subject><subject>River forecasting</subject><subject>Rivers</subject><subject>Sediment</subject><subject>Sediment discharge</subject><subject>Sediments</subject><subject>Suspended sediments</subject><issn>1571-5124</issn><issn>1814-2060</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNp9kEFLAzEQhYMoWKs_QQh43jqzSXaTm6VoLRS86Dlkk6xN6e7WZKv037tL69XDMHN47w3vI-QeYYYg4RFFiQJzPssByxkyFBLFBZmgRJ7lUMDlcA-abBRdk5uUtgCiEBwmZD6nm2MVg6ON7zedo3UXx_HWpD60nzSGbx-zdEh73zrvaPIuNL7tEw0tXUXT3pKr2uySvzvvKfl4eX5fvGbrt-VqMV9nljHZZ9455Kzktaq4sQhGGlO7XIgKlZB5rTgDYZFZo7iUHMpC5SVXvPBVBRYMm5KHU-4-dl8Hn3q97Q6xHV5qVAVyUIzng0qcVDZ2KUVf630MjYlHjaBHWvqPlh5p6TOtwfd08oV2aN-Yny7unO7NcdfFemhpQ9Ls_4hfY11vmQ</recordid><startdate>20171002</startdate><enddate>20171002</enddate><creator>Tavakoli Targhi, Alireza</creator><creator>Abbaszadeh, Sadegh</creator><creator>Arabasadi, Zeinab</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QH</scope><scope>7TG</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H96</scope><scope>H97</scope><scope>KL.</scope><scope>KR7</scope><scope>L.G</scope><orcidid>https://orcid.org/0000-0002-8257-0116</orcidid></search><sort><creationdate>20171002</creationdate><title>A hybrid method for forecasting river-suspended sediments in Iran</title><author>Tavakoli Targhi, Alireza ; Abbaszadeh, Sadegh ; Arabasadi, Zeinab</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-edd14374f9b4ac10a8aafd255b19582f94305c13ca948840769274946ebb0c0a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>artificial neural network</topic><topic>Artificial neural networks</topic><topic>Correlation coefficient</topic><topic>Correlation coefficients</topic><topic>cuckoo optimization algorithm</topic><topic>discharge</topic><topic>Fluvial sediments</topic><topic>Forecasting</topic><topic>Hydrology</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Mathematical models</topic><topic>Methods</topic><topic>Neural networks</topic><topic>River forecasting</topic><topic>Rivers</topic><topic>Sediment</topic><topic>Sediment discharge</topic><topic>Sediments</topic><topic>Suspended sediments</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tavakoli Targhi, Alireza</creatorcontrib><creatorcontrib>Abbaszadeh, Sadegh</creatorcontrib><creatorcontrib>Arabasadi, Zeinab</creatorcontrib><collection>CrossRef</collection><collection>Aqualine</collection><collection>Meteorological & Geoastrophysical 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) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 3: Aquatic Pollution & Environmental Quality</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><jtitle>International journal of river basin management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tavakoli Targhi, Alireza</au><au>Abbaszadeh, Sadegh</au><au>Arabasadi, Zeinab</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A hybrid method for forecasting river-suspended sediments in Iran</atitle><jtitle>International journal of river basin management</jtitle><date>2017-10-02</date><risdate>2017</risdate><volume>15</volume><issue>4</issue><spage>453</spage><epage>460</epage><pages>453-460</pages><issn>1571-5124</issn><eissn>1814-2060</eissn><abstract>Estimation of sediment mass carried by rivers is an important issue in Hydrological Sciences. The main purpose of this research was to find an appropriate method to compute sediment discharge. Some machine-learning approaches have been used to forecast river-suspended sediments, correctly. One of the most effective and traditional approaches for forecasting events is to use artificial neural networks (ANNs). So, we are going to improve the performance of ANNs in estimation of suspended sediments, upon a data of Baba Aman basin in Iran. We first apply a typical neural network and obtain the root-mean-square-error of
and the correlation coefficient of
. Then, to improve the prediction ability of ANNs, we hybridize this method with cuckoo optimization algorithm (COA). Combination of ANNs with COA causes reduction in root-mean-square-error to
, increasing in correlation coefficient to
and also proposing a better model.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/15715124.2017.1315815</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-8257-0116</orcidid></addata></record> |
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| language | eng |
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| source | Taylor & Francis Journals Complete |
| subjects | artificial neural network Artificial neural networks Correlation coefficient Correlation coefficients cuckoo optimization algorithm discharge Fluvial sediments Forecasting Hydrology Learning algorithms Machine learning Mathematical models Methods Neural networks River forecasting Rivers Sediment Sediment discharge Sediments Suspended sediments |
| title | A hybrid method for forecasting river-suspended sediments in Iran |
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