Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria
Water resources, land and soil degradation, desertification, agricultural productivity, and food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. The modeling results...
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| Format: | Article |
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MDPI AG
2023-02-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/15/4/765 |
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| author | Mohammed Achite Nehal Elshaboury Muhammad Jehanzaib Dinesh Kumar Vishwakarma Quoc Bao Pham Duong Tran Anh Eslam Mohammed Abdelkader Ahmed Elbeltagi |
| author_facet | Mohammed Achite Nehal Elshaboury Muhammad Jehanzaib Dinesh Kumar Vishwakarma Quoc Bao Pham Duong Tran Anh Eslam Mohammed Abdelkader Ahmed Elbeltagi |
| author_sort | Mohammed Achite |
| collection | DOAJ |
| description | Water resources, land and soil degradation, desertification, agricultural productivity, and food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. The modeling results for SPI at 3, 6, 9, and 12 months are based on five types of machine learning: support vector machine (SVM), additive regression, bagging, random subspace, and random forest. After training, testing, and cross-validation at five folds on sub-basin 1, the results concluded that SVM is the most effective model for predicting SPI for different months (3, 6, 9, and 12). Then, SVM, as the best model, was applied on sub-basin 2 for predicting SPI at different timescales and it achieved satisfactory outcomes. Its performance was validated on sub-basin 2 and satisfactory results were achieved. The suggested model performed better than the other models for estimating drought at sub-basins during the testing phase. The suggested model could be used to predict meteorological drought on several timescales, choose remedial measures for research basin, and assist in the management of sustainable water resources. |
| first_indexed | 2024-03-11T07:59:43Z |
| format | Article |
| id | doaj.art-6f0ae10a1c9245ebb159c370cf0315b9 |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-11T07:59:43Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-6f0ae10a1c9245ebb159c370cf0315b92023-11-16T23:53:12ZengMDPI AGWater2073-44412023-02-0115476510.3390/w15040765Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, AlgeriaMohammed Achite0Nehal Elshaboury1Muhammad Jehanzaib2Dinesh Kumar Vishwakarma3Quoc Bao Pham4Duong Tran Anh5Eslam Mohammed Abdelkader6Ahmed Elbeltagi7Laboratory of Water and Environment, Faculty of Nature and Life Sciences, Hassiba Benbouali University of Chlef, Chlef 02180, AlgeriaHousing and Building National Research Centre, Construction and Project Management Research Institute, Giza 12311, EgyptResearch Institute of Engineering and Technology, Hanyang University, Ansan 15588, Republic of KoreaDepartment of Irrigation and Drainage Engineering, G.B. Pant, University of Agriculture and Technology, Pantnagar 263145, IndiaInstitute of Applied Technology, Thu Dau Mot University, Thu Dau Mot City 75000, VietnamLaboratory of Environmental Sciences and Climate Change, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, VietnamDepartment of Building and Real Estate, Faculty of Construction and Environment, The Hong Kong Polytechnic University, ZN716 Block Z Phase 8 Hung Hom, Kowloon 999077, Hong KongAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura 35516, EgyptWater resources, land and soil degradation, desertification, agricultural productivity, and food security are all adversely influenced by drought. The prediction of meteorological droughts using the standardized precipitation index (SPI) is crucial for water resource management. The modeling results for SPI at 3, 6, 9, and 12 months are based on five types of machine learning: support vector machine (SVM), additive regression, bagging, random subspace, and random forest. After training, testing, and cross-validation at five folds on sub-basin 1, the results concluded that SVM is the most effective model for predicting SPI for different months (3, 6, 9, and 12). Then, SVM, as the best model, was applied on sub-basin 2 for predicting SPI at different timescales and it achieved satisfactory outcomes. Its performance was validated on sub-basin 2 and satisfactory results were achieved. The suggested model performed better than the other models for estimating drought at sub-basins during the testing phase. The suggested model could be used to predict meteorological drought on several timescales, choose remedial measures for research basin, and assist in the management of sustainable water resources.https://www.mdpi.com/2073-4441/15/4/765meteorological droughtsemi-arid regionssupport vector machineadditive regressionbaggingrandom subspace |
| spellingShingle | Mohammed Achite Nehal Elshaboury Muhammad Jehanzaib Dinesh Kumar Vishwakarma Quoc Bao Pham Duong Tran Anh Eslam Mohammed Abdelkader Ahmed Elbeltagi Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria Water meteorological drought semi-arid regions support vector machine additive regression bagging random subspace |
| title | Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria |
| title_full | Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria |
| title_fullStr | Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria |
| title_full_unstemmed | Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria |
| title_short | Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria |
| title_sort | performance of machine learning techniques for meteorological drought forecasting in the wadi mina basin algeria |
| topic | meteorological drought semi-arid regions support vector machine additive regression bagging random subspace |
| url | https://www.mdpi.com/2073-4441/15/4/765 |
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