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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
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    References listed on IDEAS

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    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.
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