Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environment...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8844367 |
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| author | Hai Tao Ali Omran Al-Sulttani Ameen Mohammed Salih Ameen Zainab Hasan Ali Nadhir Al-Ansari Sinan Q. Salih Reham R. Mostafa |
| author_facet | Hai Tao Ali Omran Al-Sulttani Ameen Mohammed Salih Ameen Zainab Hasan Ali Nadhir Al-Ansari Sinan Q. Salih Reham R. Mostafa |
| author_sort | Hai Tao |
| collection | DOAJ |
| description | The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models. |
| format | Article |
| id | doaj-art-18274ab1aa554f9e9c66b9647cc10b03 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-18274ab1aa554f9e9c66b9647cc10b032025-02-03T06:05:28ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88443678844367Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow ForecastingHai Tao0Ali Omran Al-Sulttani1Ameen Mohammed Salih Ameen2Zainab Hasan Ali3Nadhir Al-Ansari4Sinan Q. Salih5Reham R. Mostafa6Computer Science Department, Baoji University of Arts and Sciences, Baoji, Shaanxi, ChinaDepartment of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad, IraqDepartment of Water Resources Engineering, College of Engineering, University of Baghdad, Baghdad, IraqCivil Engineering Department, College of Engineering, University of Diyala, Baquba, IraqCivil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187 Lulea, SwedenInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamInformation Systems Department, Faculty of Computers and Information Sciences, Mansoura University, Mansoura 35516, EgyptThe hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized support vector regression model with a genetic algorithm (SVR-GA) over the other ML forecasting models for monthly river flow forecasting using 90%–10% data division. In addition, it was found to improve the accuracy in forecasting high flow events. The unique architecture of developed SVR-GA due to the ability of the GA optimizer to tune the internal parameters of the SVR model provides a robust learning process. This has made it more efficient in forecasting stochastic river flow behaviour compared to the other developed hybrid models.http://dx.doi.org/10.1155/2020/8844367 |
| spellingShingle | Hai Tao Ali Omran Al-Sulttani Ameen Mohammed Salih Ameen Zainab Hasan Ali Nadhir Al-Ansari Sinan Q. Salih Reham R. Mostafa Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting Complexity |
| title | Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting |
| title_full | Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting |
| title_fullStr | Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting |
| title_full_unstemmed | Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting |
| title_short | Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting |
| title_sort | training and testing data division influence on hybrid machine learning model process application of river flow forecasting |
| url | http://dx.doi.org/10.1155/2020/8844367 |
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