Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model
Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radia...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/7/1437 |
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| author | Behrooz Keshtegar Jamshid Piri Waqas Ul Hussan Kamran Ikram Muhammad Yaseen Ozgur Kisi Rana Muhammad Adnan Muhammad Adnan Muhammad Waseem |
| author_facet | Behrooz Keshtegar Jamshid Piri Waqas Ul Hussan Kamran Ikram Muhammad Yaseen Ozgur Kisi Rana Muhammad Adnan Muhammad Adnan Muhammad Waseem |
| author_sort | Behrooz Keshtegar |
| collection | DOAJ |
| description | Reliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R<sup>2</sup>), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R<sup>2</sup>, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively. |
| first_indexed | 2024-03-11T05:20:58Z |
| format | Article |
| id | doaj.art-7ebc655b3ea843318f9b5fe7a9619a20 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T05:20:58Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-7ebc655b3ea843318f9b5fe7a9619a202023-11-17T17:50:50ZengMDPI AGWater2073-44412023-04-01157143710.3390/w15071437Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree ModelBehrooz Keshtegar0Jamshid Piri1Waqas Ul Hussan2Kamran Ikram3Muhammad Yaseen4Ozgur Kisi5Rana Muhammad Adnan6Muhammad Adnan7Muhammad Waseem8Department of Civil Engineering, Faculty of Engineering, University of Zabol, Zabol 9861335856, IranDepartment of Water Engineering, Faculty of Water and Soil, University of Zabol, Zabol 9861335856, IranDepartment of Irrigation and Drainage, University of Agriculture, DI Khan 29111, PakistanDepartment of Agricultural Engineering, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, PakistanCentre for Integrated Mountain Research (CIMR), Qaid e Azam Campus, University of the Punjab, Lahore 53720, PakistanDepartment of Civil Engineering, University of Applied Sciences, 23562 Lübeck, GermanySchool of Economics and Statistics, Guangzhou University, Guangzhou 510006, ChinaInstitute of International Rivers and Eco-Security, Yunnan University, Kunming 650091, ChinaCentre of Excellence in Water Resources Engineering (CEWRE), University of Engineering & Technology, Lahore 54890, PakistanReliable estimations of sediment yields are very important for investigations of river morphology and water resources management. Nowadays, soft computing methods are very helpful and famous regarding the accurate estimation of sediment loads. The present study checked the applicability of the radial M5 tree (RM5Tree) model to accurately estimate sediment yields using daily inputs of the snow cover fraction, air temperature, evapotranspiration and effective rainfall, in addition to the flow, in the Gilgit River, Upper Indus Basin (UIB) tributary, Pakistan. The results of the RM5Tree model were compared with support vector regression (SVR), artificial neural network (ANN), multivariate adaptive regression spline (MARS), M5Tree, sediment rating curve (SRC) and response surface method (RSM) models. The resulting accuracy of the models was assessed using Pearson’s correlation coefficient (R<sup>2</sup>), the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE). The prediction accuracy of the RM5Tree model during the testing period was superior to the ANN, MARS, SVR, M5Tree, RSM and SRC models with the R<sup>2</sup>, RMSE and MAPE being 0.72, 0.51 tons/day and 11.99%, respectively. The RM5Tree model predicted suspended sediment peaks better, with 84.10% relative accuracy, in comparison to the MARS, ANN, SVR, M5Tree, RSM and SRC models, with 80.62, 77.86, 81.90, 80.20, 74.58 and 62.49% relative accuracies, respectively.https://www.mdpi.com/2073-4441/15/7/1437Gilgit Riversnowmeltssuspended sediment yieldsM5TreeRM5TreeUpper Indus Basin (UIB) |
| spellingShingle | Behrooz Keshtegar Jamshid Piri Waqas Ul Hussan Kamran Ikram Muhammad Yaseen Ozgur Kisi Rana Muhammad Adnan Muhammad Adnan Muhammad Waseem Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model Water Gilgit River snowmelts suspended sediment yields M5Tree RM5Tree Upper Indus Basin (UIB) |
| title | Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model |
| title_full | Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model |
| title_fullStr | Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model |
| title_full_unstemmed | Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model |
| title_short | Prediction of Sediment Yields Using a Data-Driven Radial M5 Tree Model |
| title_sort | prediction of sediment yields using a data driven radial m5 tree model |
| topic | Gilgit River snowmelts suspended sediment yields M5Tree RM5Tree Upper Indus Basin (UIB) |
| url | https://www.mdpi.com/2073-4441/15/7/1437 |
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