Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105790
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Building and Real Estate | - |
dc.creator | Achite, M | - |
dc.creator | Elshaboury, N | - |
dc.creator | Jehanzaib, M | - |
dc.creator | Vishwakarma, DK | - |
dc.creator | Pham, QB | - |
dc.creator | Anh, DT | - |
dc.creator | Abdelkader, EM | - |
dc.creator | Elbeltagi, A | - |
dc.date.accessioned | 2024-04-23T04:31:19Z | - |
dc.date.available | 2024-04-23T04:31:19Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105790 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.rights | Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Achite M, Elshaboury N, Jehanzaib M, Vishwakarma DK, Pham QB, Anh DT, Abdelkader EM, Elbeltagi A. Performance of Machine Learning Techniques for Meteorological Drought Forecasting in the Wadi Mina Basin, Algeria. Water. 2023; 15(4):765 is available at https://doi.org/10.3390/w15040765. | en_US |
dc.subject | Additive regression | en_US |
dc.subject | Bagging | en_US |
dc.subject | Meteorological drought | en_US |
dc.subject | Random forest | en_US |
dc.subject | Random subspace | en_US |
dc.subject | Semi-arid regions | en_US |
dc.subject | Support vector machine | en_US |
dc.title | Performance of machine learning techniques for meteorological drought forecasting in the Wadi Mina Basin, Algeria | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 4 | - |
dc.identifier.doi | 10.3390/w15040765 | - |
dcterms.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. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Water (Switzerland), Feb. 2023, v. 15, no. 4, 765 | - |
dcterms.isPartOf | Water (Switzerland) | - |
dcterms.issued | 2023-02 | - |
dc.identifier.scopus | 2-s2.0-85149258932 | - |
dc.identifier.eissn | 2073-4441 | - |
dc.identifier.artn | 765 | - |
dc.description.validate | 202404 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
water-15-00765-v3.pdf | 6.1 MB | Adobe PDF | View/Open |
Page views
6
Citations as of Jun 30, 2024
SCOPUSTM
Citations
18
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
18
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.