Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations
Decision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorit...
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my.uniten.dspace-268952023-05-29T17:37:38Z Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations Kumar M. Elbeltagi A. Pande C.B. Ahmed A.N. Chow M.F. Pham Q.B. Kumari A. Kumar D. 57806584200 57204724397 57193547008 57214837520 57214146115 57208495034 57219317550 57212688058 Decision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorithm; error analysis; estimation method; flood control; modeling; river discharge; river flow; India Accurate and reliable discharge estimation is considered vital in managing water resources, agriculture, industry, and flood management on the basin scale. In this study, five data-driven tree-based algorithms: M5-Pruned model-M5P (Model-1), Random Forest-RF (Model-2), Random Tree-RT (Model-3), Reduced Error Pruning Tree-REP Tree (Model-4), and Decision Stump-DS (Model-5) have been examined to measure the daily discharge of Govindpur site at Burhabalang river, India. The proposed models will be calibrated by daily 10-years time-series hydrological data (i.e., river stage (h) and daily discharge (Q)) measured from 2004 to 2013. In these models, 70% and 30% of the dataset were used for the training and testing stage for the reliability of the developed models. The precision of the models was optimized by investigating five different scenarios based on various time-lags combinations. Model�s performance has been assessed and evaluated using five statistical metrics, namely, correlation coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Root Relative Squared Error (RRSE). Results showed that Model-3 outperforms as compared to other proposed models. Machine learning models have been examined five scenarios of input variables during training and testing phases. In comparison of the Model-5 struggled in capturing the river's flow rate and showed poor performance in scenarios where R2 metric values ranged from 0.64 to 0.94. Therefore, it can be concluded that the RT model could be used as a robust model for sustainable flood plain management. � 2022, The Author(s), under exclusive licence to Springer Nature B.V. Final 2023-05-29T09:37:38Z 2023-05-29T09:37:38Z 2022 Article 10.1007/s11269-022-03136-x 2-s2.0-85128754241 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128754241&doi=10.1007%2fs11269-022-03136-x&partnerID=40&md5=7aaee30b8ac7be3921a9487f7722a3f2 https://irepository.uniten.edu.my/handle/123456789/26895 36 7 2201 2221 Springer Science and Business Media B.V. Scopus |
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Decision trees; Errors; Flood control; Floods; Mean square error; Statistical tests; Agriculture management; Burhabalang river; Daily discharge; Data-driven model; Discharge estimation; Flood management; Training and testing; Water flood; Water industries; Water resources management; Rivers; algorithm; error analysis; estimation method; flood control; modeling; river discharge; river flow; India |
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57806584200 Kumar M. Elbeltagi A. Pande C.B. Ahmed A.N. Chow M.F. Pham Q.B. Kumari A. Kumar D. |
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Kumar M. Elbeltagi A. Pande C.B. Ahmed A.N. Chow M.F. Pham Q.B. Kumari A. Kumar D. |
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Kumar M. Elbeltagi A. Pande C.B. Ahmed A.N. Chow M.F. Pham Q.B. Kumari A. Kumar D. Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
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Kumar M. |
| title |
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
| title_short |
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
| title_full |
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
| title_fullStr |
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
| title_full_unstemmed |
Applications of Data-driven Models for Daily Discharge Estimation Based on Different Input Combinations |
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applications of data-driven models for daily discharge estimation based on different input combinations |
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Springer Science and Business Media B.V. |
| publishDate |
2023 |
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