Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology
Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchm...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-09-01
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| Series: | Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809922000613 |
| _version_ | 1797388870588825600 |
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| author | Houfa Wu Jianyun Zhang Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang |
| author_facet | Houfa Wu Jianyun Zhang Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang |
| author_sort | Houfa Wu |
| collection | DOAJ |
| description | Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data. |
| first_indexed | 2024-03-08T22:46:55Z |
| format | Article |
| id | doaj.art-4238a92ae71e4bc7ab4f4eece1c2021d |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-03-08T22:46:55Z |
| publishDate | 2023-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-4238a92ae71e4bc7ab4f4eece1c2021d2023-12-17T06:38:14ZengElsevierEngineering2095-80992023-09-012893104Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization MethodologyHoufa Wu0Jianyun Zhang1Zhenxin Bao2Guoqing Wang3Wensheng Wang4Yanqing Yang5Jie Wang6State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; State Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, ChinaState Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, ChinaState Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China; Corresponding author.State Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, ChinaState Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource and Hydropower, Sichuan University, Chengdu 610065, China; State Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, ChinaState Key Laboratory of Hydrology−Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China; Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, ChinaModel parameters estimation is a pivotal issue for runoff modeling in ungauged catchments. The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization, which is the most widely used approach. Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin (YHHRB). The values of the Nash–Sutcliffe efficiency coefficient (NSE), coefficient of determination (R2), and percent bias (PBIAS) indicated the acceptable performance of the soil and water assessment tool (SWAT) model in the YHHRB. Nine descriptors belonging to the categories of climate, soil, vegetation, and topography were used to express the catchment characteristics related to the hydrological processes. The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models, including linear regression (LR) equations, support vector regression (SVR), random forest (RF), k-nearest neighbor (kNN), decision tree (DT), and radial basis function (RBF). Each of the 38 catchments was assumed to be an ungauged catchment in turn. Then, the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments. Furthermore, the similarity-based regionalization scheme was used for comparison with the regression-based approach. The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments. Compared with the traditional LR-based approach, the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships. The performances of different approaches were similar in humid regions, while the advantages of the machine learning techniques were more evident in arid regions. When the study area contained nested catchments, the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance. The new findings could improve flood forecasting and water resources planning in regions that lack observed data.http://www.sciencedirect.com/science/article/pii/S2095809922000613Parameters estimationUngauged catchmentsRegionalization schemeMachine learning algorithmsSoil and water assessment tool model |
| spellingShingle | Houfa Wu Jianyun Zhang Zhenxin Bao Guoqing Wang Wensheng Wang Yanqing Yang Jie Wang Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology Engineering Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and water assessment tool model |
| title | Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology |
| title_full | Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology |
| title_fullStr | Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology |
| title_full_unstemmed | Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology |
| title_short | Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology |
| title_sort | runoff modeling in ungauged catchments using machine learning algorithm based model parameters regionalization methodology |
| topic | Parameters estimation Ungauged catchments Regionalization scheme Machine learning algorithms Soil and water assessment tool model |
| url | http://www.sciencedirect.com/science/article/pii/S2095809922000613 |
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