A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers
Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs...
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| Veröffentlicht in: | Acta geophysica 2019-02, Vol.67 (1), p.205-214 |
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| creator | Varvani, J. Khaleghi, M. R. |
| description | Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods. |
| doi_str_mv | 10.1007/s11600-018-0228-9 |
| format | Article |
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R.</creator><creatorcontrib>Varvani, J. ; Khaleghi, M. R.</creatorcontrib><description>Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. Also, the average value of the AI of MSRCs is 1.15 which is an acceptable amount of the other considered various methods.</description><identifier>ISSN: 1895-6572</identifier><identifier>EISSN: 1895-7455</identifier><identifier>DOI: 10.1007/s11600-018-0228-9</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Artificial neural networks ; Climatic zones ; Data analysis ; Earth and Environmental Science ; Earth Sciences ; Fluvial sediments ; Fuzzy logic ; Geophysics/Geodesy ; Geotechnical Engineering & Applied Earth Sciences ; Hydrologic models ; Hydrology ; Hydrometric stations ; Load distribution ; Performance evaluation ; Performance indices ; Regression analysis ; Regression models ; Research Article - Hydrology ; River sediments ; Rivers ; Sediment load ; Sediments ; Stations ; Structural Geology ; Watersheds</subject><ispartof>Acta geophysica, 2019-02, Vol.67 (1), p.205-214</ispartof><rights>Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2018</rights><rights>Acta Geophysica is a copyright of Springer, (2018). 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R.</creatorcontrib><title>A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers</title><title>Acta geophysica</title><addtitle>Acta Geophys</addtitle><description>Sediment rating curves (SRCs) have been recognized as the most popular method for estimating sediment in the hydrology of river sediments and in watersheds. In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. 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In this regard, in order to compare and correct estimation methods of river sediment load, estimated rates of several univariate types of SRCs and a multivariate type of SRCs (MSRCs) were studied using the neuro-fuzzy and tree regression models in five selective hydrometric stations of different climatic zones of Iran and with various indexes of the accuracy (AI) and the precision (PI). The results of the data analysis showed that the mean of the AI of neuro-fuzzy and tree regression models in selective stations is 151 and 536%, respectively, which shows the low efficiency compared with SRCs. Also according to the results, the best rate of the AI of the MSRCs belongs to the Glink station with the rate of 1.12. 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| subjects | Artificial neural networks Climatic zones Data analysis Earth and Environmental Science Earth Sciences Fluvial sediments Fuzzy logic Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Hydrologic models Hydrology Hydrometric stations Load distribution Performance evaluation Performance indices Regression analysis Regression models Research Article - Hydrology River sediments Rivers Sediment load Sediments Stations Structural Geology Watersheds |
| title | A performance evaluation of neuro-fuzzy and regression methods in estimation of sediment load of selective rivers |
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