Performance of machine learning techniques for meteorological drought forecasting in the Wadi Mina Basin, Algeria

Mohammed Achite, Nehal Elshaboury, Muhammad Jehanzaib, Dinesh Kumar Vishwakarma, Quoc Bao Pham*, Duong Tran Anh, Eslam Mohammed Abdelkader, Ahmed Elbeltagi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)
20 Downloads (Pure)

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 languageEnglish
Article number765
Number of pages20
JournalWater (Switzerland)
Volume15
Issue number4
DOIs
Publication statusPublished - 15 Feb 2023
Externally publishedYes

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

Fingerprint

Dive into the research topics of 'Performance of machine learning techniques for meteorological drought forecasting in the Wadi Mina Basin, Algeria'. Together they form a unique fingerprint.

Cite this