Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)
Sediment transport constantly influences river and civil structures and the lack ofinformation about its exact amount makes management efforts less effective. Hence,achieving a proper procedure to estimate the sediment load in rivers is important. We usedsupport vector machine model to estimate the...
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| Format: | Article |
| Language: | English |
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Gorgan University of Agricultural Sciences and Natural Resources
2018-07-01
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| Series: | Environmental Resources Research |
| Subjects: | |
| Online Access: | https://ijerr.gau.ac.ir/article_4321_4d6d3d532962a4f1d961b6cb061090cc.pdf |
| _version_ | 1797311369379315712 |
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| author | H. Torabi R. Dehghani |
| author_facet | H. Torabi R. Dehghani |
| author_sort | H. Torabi |
| collection | DOAJ |
| description | Sediment transport constantly influences river and civil structures and the lack ofinformation about its exact amount makes management efforts less effective. Hence,achieving a proper procedure to estimate the sediment load in rivers is important. We usedsupport vector machine model to estimate the sediments of the Kakareza River in LorestanProvince and the results were compared with those obtained by gene expressionprogramming. The parameter of flow discharge for input in different time lags and theparameter of sediment for output during 1992-2012 were considered. Criteria includingcorrelation coefficient, root mean square error and mean absolute error were used toevaluate and also compare the performance of models. With regards to accuracy, thesupport vector machine model showed the highest correlation coefficient (0.994), minimumroot mean square error (0.001 ton/day) and the mean absolute error (0.001 ton/day) whichwas initiated at verification stage. The results also showed that the support vector machinehas great capability to estimate the minimum and maximum values for sediment discharge. |
| first_indexed | 2024-03-08T01:57:39Z |
| format | Article |
| id | doaj.art-33599e3e95894437a596e168601f4aca |
| institution | Directory Open Access Journal |
| issn | 2783-4832 2783-4670 |
| language | English |
| last_indexed | 2024-03-08T01:57:39Z |
| publishDate | 2018-07-01 |
| publisher | Gorgan University of Agricultural Sciences and Natural Resources |
| record_format | Article |
| series | Environmental Resources Research |
| spelling | doaj.art-33599e3e95894437a596e168601f4aca2024-02-14T08:36:00ZengGorgan University of Agricultural Sciences and Natural ResourcesEnvironmental Resources Research2783-48322783-46702018-07-016213914810.22069/ijerr.2018.12245.11824321Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran)H. Torabi0R. Dehghani1Lorestan University, Khorramabad, IranLorestan University, Khorramabad, IranSediment transport constantly influences river and civil structures and the lack ofinformation about its exact amount makes management efforts less effective. Hence,achieving a proper procedure to estimate the sediment load in rivers is important. We usedsupport vector machine model to estimate the sediments of the Kakareza River in LorestanProvince and the results were compared with those obtained by gene expressionprogramming. The parameter of flow discharge for input in different time lags and theparameter of sediment for output during 1992-2012 were considered. Criteria includingcorrelation coefficient, root mean square error and mean absolute error were used toevaluate and also compare the performance of models. With regards to accuracy, thesupport vector machine model showed the highest correlation coefficient (0.994), minimumroot mean square error (0.001 ton/day) and the mean absolute error (0.001 ton/day) whichwas initiated at verification stage. The results also showed that the support vector machinehas great capability to estimate the minimum and maximum values for sediment discharge.https://ijerr.gau.ac.ir/article_4321_4d6d3d532962a4f1d961b6cb061090cc.pdfsuspended sedimentkakarezasupport vector machinegene expression programing |
| spellingShingle | H. Torabi R. Dehghani Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) Environmental Resources Research suspended sediment kakareza support vector machine gene expression programing |
| title | Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) |
| title_full | Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) |
| title_fullStr | Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) |
| title_full_unstemmed | Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) |
| title_short | Comparison and evaluation of intelligent models for river suspended sediment estimation (case study: Kakareza River, Iran) |
| title_sort | comparison and evaluation of intelligent models for river suspended sediment estimation case study kakareza river iran |
| topic | suspended sediment kakareza support vector machine gene expression programing |
| url | https://ijerr.gau.ac.ir/article_4321_4d6d3d532962a4f1d961b6cb061090cc.pdf |
| work_keys_str_mv | AT htorabi comparisonandevaluationofintelligentmodelsforriversuspendedsedimentestimationcasestudykakarezariveriran AT rdehghani comparisonandevaluationofintelligentmodelsforriversuspendedsedimentestimationcasestudykakarezariveriran |