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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Bibliographic Details
Published in:Water (Basel) 2023-02, Vol.15 (4), p.765
Main Authors: Achite, Mohammed, Elshaboury, Nehal, Jehanzaib, Muhammad, Vishwakarma, Dinesh, Pham, Quoc, Anh, Duong, Abdelkader, Eslam, Elbeltagi, Ahmed
Format: Article
Language:English
Subjects:
Accuracy
Agricultural production
Algeria
Algorithms
Analysis
Aquatic resources
Basins
Desertification
Drought
Drought forecasting
Droughts
Environmental degradation
Food security
Food supply
Forecasting
Hydrology
Learning algorithms
Machine learning
Management
Methods
Optimization
Parameter estimation
Precipitation
Precipitation (Meteorology)
Rain
Resource management
Runoff
Soil degradation
Soil water
Standardized precipitation index
Support vector machines
Sustainability management
Topography
Water
Water management
Water resources
Water resources management
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Summary: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.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15040765