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Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm

algorithm; drought; machine learning; modeling; optimization; prediction; support vector machine; water management; Iran; Euphausiacea

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Main Authors: Mohamadi S., Sammen S.S., Panahi F., Ehteram M., Kisi O., Mosavi A., Ahmed A.N., El-Shafie A., Al-Ansari N.
Other Authors: 57194149742
Format: Article
Published: Springer 2023
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spelling my.uniten.dspace-252112023-05-29T16:07:23Z Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm Mohamadi S. Sammen S.S. Panahi F. Ehteram M. Kisi O. Mosavi A. Ahmed A.N. El-Shafie A. Al-Ansari N. 57194149742 57192093108 55368172500 57113510800 6507051085 57191408081 57214837520 16068189400 51664437800 algorithm; drought; machine learning; modeling; optimization; prediction; support vector machine; water management; Iran; Euphausiacea The modelling of drought is of utmost importance for the efficient management of water resources. This article used the adaptive neuro-fuzzy interface system (ANFIS), multilayer perceptron (MLP), radial basis function neural network (RBFNN), and support vector machine (SVM) models to forecast meteorological droughts in Iran. The spatial�temporal pattern of droughts in Iran was also found using recorded observation data from 1980 to 2014. A nomadic people algorithm (NPA) was utilized to train the ANFIS, MLP, RBFNN, and SVM models. Additionally, the NPA was benchmarked against the bat algorithm, salp swarm algorithm, and krill algorithm (KA). The hybrid ANFIS, MLP, RBFNN, and SVM models were used to forecast the 3-month standardized precipitation index. New evolutionary algorithms were utilized to improve the convergence speed of the soft computing models and their accuracy. First, random stations, namely, in Azarbayjan (northwest Iran), Khouzestan (southwest Iran), Khorasan (northeast Iran), and Sistan and Balouchestan (southeast Iran) were selected for the testing of the models. According to the results obtained from the Azarbayjan station, the Nash�Sutcliffe efficiency (NSE) was 0.93, 0.86, 0.85, and 0.83 for the ANFIS�NPA, MLP�NPA, RBFNN�NPA, and SVM�NPA models, respectively. For Sistan and Baloucehstan, the results indicated the superiority of the ANFIS�NPA model, followed by the MLP�NPA model, compared to the RBFNN�NPA and SVM�NPA models, and suggested that the hybrid models performed better than the standalone MLP, RBFNN, ANFIS, and SVM models. The second aim of the study was to capture the relationship between large-scale climate signals and drought indices by using a wavelet coherence analysis. The general results indicated that the NPA and wavelet coherence analysis are useful tools for modelling drought indices. � 2020, Springer Nature B.V. Final 2023-05-29T08:07:23Z 2023-05-29T08:07:23Z 2020 Article 10.1007/s11069-020-04180-9 2-s2.0-85089579917 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089579917&doi=10.1007%2fs11069-020-04180-9&partnerID=40&md5=b48189a8baa7938c3ded4aac3eea00f0 https://irepository.uniten.edu.my/handle/123456789/25211 104 1 537 579 All Open Access, Green Springer Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description algorithm; drought; machine learning; modeling; optimization; prediction; support vector machine; water management; Iran; Euphausiacea
author2 57194149742
author_facet 57194149742
Mohamadi S.
Sammen S.S.
Panahi F.
Ehteram M.
Kisi O.
Mosavi A.
Ahmed A.N.
El-Shafie A.
Al-Ansari N.
format Article
author Mohamadi S.
Sammen S.S.
Panahi F.
Ehteram M.
Kisi O.
Mosavi A.
Ahmed A.N.
El-Shafie A.
Al-Ansari N.
spellingShingle Mohamadi S.
Sammen S.S.
Panahi F.
Ehteram M.
Kisi O.
Mosavi A.
Ahmed A.N.
El-Shafie A.
Al-Ansari N.
Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
author_sort Mohamadi S.
title Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
title_short Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
title_full Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
title_fullStr Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
title_full_unstemmed Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
title_sort zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm
publisher Springer
publishDate 2023
_version_ 1806426465217019904
score 13.222552

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