Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads
The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and reso...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/5/1481 |
| _version_ | 1797567247179317248 |
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| author | Waqas Ul Hussan Muhammad Khurram Shahzad Frank Seidel Franz Nestmann |
| author_facet | Waqas Ul Hussan Muhammad Khurram Shahzad Frank Seidel Franz Nestmann |
| author_sort | Waqas Ul Hussan |
| collection | DOAJ |
| description | The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R<sup>2</sup> value of 0.85 and 0.74 during the training and testing period, respectively. |
| first_indexed | 2024-03-10T19:38:58Z |
| format | Article |
| id | doaj.art-b9e149ae49cb468d8e210432062f3479 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T19:38:58Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-b9e149ae49cb468d8e210432062f34792023-11-20T01:25:39ZengMDPI AGWater2073-44412020-05-01125148110.3390/w12051481Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment LoadsWaqas Ul Hussan0Muhammad Khurram Shahzad1Frank Seidel2Franz Nestmann3Institute for Water and River Basin Management, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, GermanyDepartment of Civil Engineering and Technology, Institute of Southern Punjab, Multan 60000, PakistanInstitute for Water and River Basin Management, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, GermanyInstitute for Water and River Basin Management, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131 Karlsruhe, GermanyThe accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R<sup>2</sup> value of 0.85 and 0.74 during the training and testing period, respectively.https://www.mdpi.com/2073-4441/12/5/1481suspended sediment concentrationsGilgit basinsnow cover fractionartificial neural networkMARS modelHindukush |
| spellingShingle | Waqas Ul Hussan Muhammad Khurram Shahzad Frank Seidel Franz Nestmann Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads Water suspended sediment concentrations Gilgit basin snow cover fraction artificial neural network MARS model Hindukush |
| title | Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads |
| title_full | Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads |
| title_fullStr | Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads |
| title_full_unstemmed | Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads |
| title_short | Application of Soft Computing Models with Input Vectors of Snow Cover Area in Addition to Hydro-Climatic Data to Predict the Sediment Loads |
| title_sort | application of soft computing models with input vectors of snow cover area in addition to hydro climatic data to predict the sediment loads |
| topic | suspended sediment concentrations Gilgit basin snow cover fraction artificial neural network MARS model Hindukush |
| url | https://www.mdpi.com/2073-4441/12/5/1481 |
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