IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v116y2023i2d10.1007_s11069-022-05779-w.html
   My bibliography  Save this article

Application of artificial intelligence hybrid models for meteorological drought prediction

Author

Listed:
  • Seyed Mohammad Ehsan Azimi

    (University of Tehran)

  • Seyed Javad Sadatinejad

    (University of Tehran)

  • Arash Malekian

    (University of Tehran)

  • Mohammad Hossein Jahangir

    (University of Tehran)

Abstract

Drought is a prolonged dry period that has a serious impact on health, agriculture, economies, energy, and the environment. Thus, there have been numerous attempts to make this phenomenon more predictable for preventing the aforementioned effects. The present study aims to determine the best combination of input data sets and predict the Standardized Precipitation Evapotranspiration Index (SPEI) in 1,6, and 12-month time scales using Artificial Intelligence (AI) models (Multilayer Perceptron Neural Network (MLPNN), Support Vector Regression (SVR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree (EDT)), which all models are hybridized with a wavelet transformation at three synoptic stations named Ardebil, Khalkhal, and Moghan. To this end, monthly lags of precipitation, temperature, and SPEI were used in northwestern Iran from 1987 to 2018. The methods were classified into single parameter and multiparameter, and each sub-method was designed based on a combination of the parameters. Moreover, Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), Effective Factor Elimination Technique (EFET), and Feature Scaling (FS) were used to determine the best lags of parameters. In this regard, Root Mean Square Error (RMSE), Correlation Coefficient (CC), and Nash–Sutcliffe Efficiency Index (NSE) were used as the statistical criteria to assess AI models, methods, and sub-methods. The results revealed that the sub-method (D) with W-MLPNN in the 1-month and 6-month time scales and the sub-method (D, P, T) with W-SVR in the 12-month time scale were the best models and sub-methods of this area, respectively. Moreover, based on the results, the efficiency of the AI was enhanced in longer time scales (more than the 6th month) and the longer the time scale, the more the number of lags (under the 6th month) in input data is decreased. Graphical Abstract

Suggested Citation

  • Seyed Mohammad Ehsan Azimi & Seyed Javad Sadatinejad & Arash Malekian & Mohammad Hossein Jahangir, 2023. "Application of artificial intelligence hybrid models for meteorological drought prediction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2565-2589, March.
    • Handle: RePEc:spr:nathaz:v:116:y:2023:i:2:d:10.1007_s11069-022-05779-w
    DOI: 10.1007/s11069-022-05779-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05779-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-022-05779-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Anurag Malik & Anil Kumar & Rajesh P. Singh, 2019. "Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(11), pages 3985-4006, September.
    2. Junfei Chen & Ming Li & Weiguang Wang, 2012. "Statistical Uncertainty Estimation Using Random Forests and Its Application to Drought Forecast," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-12, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    2. Manish Kumar & Anuradha Kumari & Daniel Prakash Kushwaha & Pravendra Kumar & Anurag Malik & Rawshan Ali & Alban Kuriqi, 2020. "Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India," Sustainability, MDPI, vol. 12(19), pages 1-21, September.
    3. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    4. Ioannis Mallidis & Volha Yakavenka & Anastasios Konstantinidis & Nikolaos Sariannidis, 2021. "A Goal Programming-Based Methodology for Machine Learning Model Selection Decisions: A Predictive Maintenance Application," Mathematics, MDPI, vol. 9(19), pages 1-16, September.
    5. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
    6. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 537-579, October.
    7. Soyeon Lim & Seungyub Lee & Donghwi Jung, 2021. "Identifying the Drought Impact Factors and Developing Drought Scenarios Using the DSD Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 4809-4823, November.
    8. Farhang Rahmani & Mohammad Hadi Fattahi, 2021. "A multifractal cross-correlation investigation into sensitivity and dependence of meteorological and hydrological droughts on precipitation and temperature," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 109(3), pages 2197-2219, December.
    9. Saeed Azimi & Erfan Hassannayebi & Morteza Boroun & Mohammad Tahmoures, 2020. "Probabilistic Analysis of Long-Term Climate Drought Using Steady-State Markov Chain Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4703-4724, December.
    10. Neeta Nandgude & T. P. Singh & Sachin Nandgude & Mukesh Tiwari, 2023. "Drought Prediction: A Comprehensive Review of Different Drought Prediction Models and Adopted Technologies," Sustainability, MDPI, vol. 15(15), pages 1-19, July.
    11. Saeid Mehdizadeh, 2020. "Using AR, MA, and ARMA Time Series Models to Improve the Performance of MARS and KNN Approaches in Monthly Precipitation Modeling under Limited Climatic Data," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(1), pages 263-282, January.
    12. Karbasi, Masoud & Jamei, Mehdi & Malik, Anurag & Kisi, Ozgur & Yaseen, Zaher Mundher, 2023. "Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model," Agricultural Water Management, Elsevier, vol. 281(C).
    13. Fatemeh Barzegari Banadkooki & Vijay P. Singh & Mohammad Ehteram, 2021. "Multi-timescale drought prediction using new hybrid artificial neural network models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2461-2478, April.
    14. Elnaz Ghabelnezam & Raoof Mostafazadeh & Zeinab Hazbavi & Guangwei Huang, 2023. "Hydrological Drought Severity in Different Return Periods in Rivers of Ardabil Province, Iran," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    15. Sinan Wang & Xigang Xing & Yingjie Wu & Jianying Guo & Mingyang Li & Bin Fu, 2024. "Seasonal Response of the NDVI to the SPEI at Different Time Scales in Yinshanbeilu, Inner Mongolia, China," Land, MDPI, vol. 13(4), pages 1-17, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:116:y:2023:i:2:d:10.1007_s11069-022-05779-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.