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

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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.

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Authors and Affiliations

  1. Department of Ecology, Institute of Science and High Technology and Environmental Sciences, Graduate University of Advanced Technology, Kerman, Iran

    Sedigheh Mohamadi

  2. Department of Civil Engineering, College of Engineering, University of Diyala, Baqubah, Diyala Governorate, Iraq

    Saad Sh. Sammen

  3. Faculty of Natural Resources and Earth Sciences, University of Kashan, Kashan, Iran

    Fatemeh Panahi

  4. Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

    Mohammad Ehteram

  5. Department of Civil Engineering, School of Technology, IIia State University, 0162, Tbilisi, Georgia

    Ozgur Kisi

  6. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Ozgur Kisi

  7. Environmental Quality, Atmospheric Science and Climate Change Research Group, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amir Mosavi

  8. Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Amir Mosavi

  9. Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), 43000, Kajang, Selangor Darul Ehsan, Malaysia

    Ali Najah Ahmed

  10. Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), 50603, Kuala Lumpur, Malaysia

    Ahmed El-Shafie

  11. National Water Center (NWC), United Arab Emirates University, P.O. Box 15551, Al Ain, UAE

    Ahmed El-Shafie

  12. Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, 97187, Luleå, Sweden

    Nadhir Al-Ansari

Authors
  1. Sedigheh Mohamadi
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  2. Saad Sh. Sammen
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  3. Fatemeh Panahi
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  4. Mohammad Ehteram
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  5. Ozgur Kisi
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  6. Amir Mosavi
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  7. Ali Najah Ahmed
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  8. Ahmed El-Shafie
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  9. Nadhir Al-Ansari
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Contributions

Conceptualization: Mohammad ehteram, Sedigheh Mohamadi, Fatemeh Panahi; Methodology: Saad Sh Sammen, Ozgur Kisi, Mohammad Ehteram; Formal analysis and investigation: Mohammad Ehteram, Amirhosein Mosavi; Writing original draft preparation: Mohammad Ehteram, Nadhir Al-Ansari, Ahmed El Shafie; Writing - review and editing: Ahmed El Shafie, Ali Najah Ahmed.

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Correspondence to Saad Sh. Sammen.

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Mohamadi, S., Sammen, S.S., Panahi, F. et al. Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm. Nat Hazards 104, 537–579 (2020). https://doi.org/10.1007/s11069-020-04180-9

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