Prediction formula for longshore sediment transport rate with M5' algorithm
One of the most vital tasks for coastal engineers is to calculate the gross longshore sediment transport rate (LSTR) to control the shoreline erosion and beach evolution. In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a functi...
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| Veröffentlicht in: | Journal of coastal research 2013-01, Vol.2 (65), p.2149-2149 |
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| creator | Mafi, Somayeh Yeganeh-Bakhtiary, Abbas Kazeminezhad, Mohammad Hossein |
| description | One of the most vital tasks for coastal engineers is to calculate the gross longshore sediment transport rate (LSTR) to control the shoreline erosion and beach evolution. In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a function of the breaking wave characteristics, bed materials and beach slope. In this present study, an alternative approach based on the Regression Trees (M5') was applied to present a new formula for prediction of LSTR in terms of the longshore sediment transport coefficient. Several high-quality data sets were employed, which comprise of the wave parameters, sediment characteristics and the longshore transport rate. Based on the obtained results, the Shields parameter was selected as the main input variable, while the longshore sediment transport coefficient was given as the output parameter. The results indicated that, the error statistics of the regression trees were less and it is evidently predicts the LSTR more accurate than the previous mentioned formulas. |
| doi_str_mv | 10.2112/SI65-363 |
| format | Article |
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In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a function of the breaking wave characteristics, bed materials and beach slope. In this present study, an alternative approach based on the Regression Trees (M5') was applied to present a new formula for prediction of LSTR in terms of the longshore sediment transport coefficient. Several high-quality data sets were employed, which comprise of the wave parameters, sediment characteristics and the longshore transport rate. Based on the obtained results, the Shields parameter was selected as the main input variable, while the longshore sediment transport coefficient was given as the output parameter. The results indicated that, the error statistics of the regression trees were less and it is evidently predicts the LSTR more accurate than the previous mentioned formulas.</description><identifier>ISSN: 0749-0208</identifier><identifier>EISSN: 1551-5036</identifier><identifier>DOI: 10.2112/SI65-363</identifier><language>eng</language><publisher>Fort Lauderdale: Allen Press Inc</publisher><subject>Algorithms ; Beaches ; Coastal ; Coastal erosion ; Coastal zone management ; Coasts ; Coefficients ; Erosion control ; Mathematical models ; Regression ; Sediment transport ; Shorelines ; Soil erosion ; Trees</subject><ispartof>Journal of coastal research, 2013-01, Vol.2 (65), p.2149-2149</ispartof><rights>Copyright Allen Press Publishing Services 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27903,27904</link.rule.ids></links><search><creatorcontrib>Mafi, Somayeh</creatorcontrib><creatorcontrib>Yeganeh-Bakhtiary, Abbas</creatorcontrib><creatorcontrib>Kazeminezhad, Mohammad Hossein</creatorcontrib><title>Prediction formula for longshore sediment transport rate with M5' algorithm</title><title>Journal of coastal research</title><description>One of the most vital tasks for coastal engineers is to calculate the gross longshore sediment transport rate (LSTR) to control the shoreline erosion and beach evolution. In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a function of the breaking wave characteristics, bed materials and beach slope. In this present study, an alternative approach based on the Regression Trees (M5') was applied to present a new formula for prediction of LSTR in terms of the longshore sediment transport coefficient. Several high-quality data sets were employed, which comprise of the wave parameters, sediment characteristics and the longshore transport rate. Based on the obtained results, the Shields parameter was selected as the main input variable, while the longshore sediment transport coefficient was given as the output parameter. The results indicated that, the error statistics of the regression trees were less and it is evidently predicts the LSTR more accurate than the previous mentioned formulas.</description><subject>Algorithms</subject><subject>Beaches</subject><subject>Coastal</subject><subject>Coastal erosion</subject><subject>Coastal zone management</subject><subject>Coasts</subject><subject>Coefficients</subject><subject>Erosion control</subject><subject>Mathematical models</subject><subject>Regression</subject><subject>Sediment transport</subject><subject>Shorelines</subject><subject>Soil 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Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mafi, Somayeh</au><au>Yeganeh-Bakhtiary, Abbas</au><au>Kazeminezhad, Mohammad Hossein</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction formula for longshore sediment transport rate with M5' algorithm</atitle><jtitle>Journal of coastal research</jtitle><date>2013-01-01</date><risdate>2013</risdate><volume>2</volume><issue>65</issue><spage>2149</spage><epage>2149</epage><pages>2149-2149</pages><issn>0749-0208</issn><eissn>1551-5036</eissn><abstract>One of the most vital tasks for coastal engineers is to calculate the gross longshore sediment transport rate (LSTR) to control the shoreline erosion and beach evolution. In the past decades, several empirical formulas or parametric models have been proposed for predicting the gross LSTR as a function of the breaking wave characteristics, bed materials and beach slope. In this present study, an alternative approach based on the Regression Trees (M5') was applied to present a new formula for prediction of LSTR in terms of the longshore sediment transport coefficient. Several high-quality data sets were employed, which comprise of the wave parameters, sediment characteristics and the longshore transport rate. Based on the obtained results, the Shields parameter was selected as the main input variable, while the longshore sediment transport coefficient was given as the output parameter. The results indicated that, the error statistics of the regression trees were less and it is evidently predicts the LSTR more accurate than the previous mentioned formulas.</abstract><cop>Fort Lauderdale</cop><pub>Allen Press Inc</pub><doi>10.2112/SI65-363</doi><tpages>1</tpages></addata></record> |
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| subjects | Algorithms Beaches Coastal Coastal erosion Coastal zone management Coasts Coefficients Erosion control Mathematical models Regression Sediment transport Shorelines Soil erosion Trees |
| title | Prediction formula for longshore sediment transport rate with M5' algorithm |
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