Abstract
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.
Original language | English |
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Article number | 765 |
Number of pages | 20 |
Journal | Water (Switzerland) |
Volume | 15 |
Issue number | 4 |
DOIs | |
Publication status | Published - 15 Feb 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2023 by the authors.
Keywords
- additive regression
- bagging
- meteorological drought
- random forest
- random subspace
- semi-arid regions
- support vector machine
ASJC Scopus subject areas
- Geography, Planning and Development
- Biochemistry
- Aquatic Science
- Water Science and Technology