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Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques

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  • K. A. Jariwala

    (Sardar Vallabhbhai National Institute of Technology)

  • P. G. Agnihotri

    (Sardar Vallabhbhai National Institute of Technology)

Abstract

Drought modeling is vital for managing water scarcity in arid regions. It allows proactive planning, resource allocation, and policy development. A combination of statistical models and machine learning techniques is necessary to capture the complexity of drought dynamics effectively. In this study, we compare the performance of the ARIMAX hybrid statistical method and the ANN and Fuzzy-based machine learning method ANFIS for drought modeling. Among the various models examined, the most promising results are obtained using a combination of ANFIS and ARIMAX, which are subsequently employed for drought event forecasting. Notably, ANFIS exhibits lower accuracy for long-term forecasting compared to ARIMAX. The study's novelty lies in the unequivocal demonstration of the ARIMAX (3,0,2) (3,0,2,12) model's superior performance in predicting meteorological drought events. This underscores the potential of ARIMAX models in leveraging historical data for adeptly forecasting drought. Furthermore, this model is applied to multiple locations to generate a drought forecasting and risk map for future years.

Suggested Citation

  • K. A. Jariwala & P. G. Agnihotri, 2023. "Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 6051-6070, December.
    • Handle: RePEc:spr:waterr:v:37:y:2023:i:15:d:10.1007_s11269-023-03642-6
    DOI: 10.1007/s11269-023-03642-6
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    References listed on IDEAS

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    1. Anas Mahmood Al-Juboori, 2023. "Prediction of Hydrological Drought in Semi-arid Regions Using a Novel Hybrid Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3657-3669, July.
    2. Yong-Sik Ham & Kyong-Bok Sonu & Un-Sim Paek & Kum-Chol Om & Sang-Il Jong & Kum-Ryong Jo, 2023. "Comparison of LSTM network, neural network and support vector regression coupled with wavelet decomposition for drought forecasting in the western area of the DPRK," 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 2619-2643, March.
    3. Anurag Malik & Anil Kumar & Sinan Q Salih & Sungwon Kim & Nam Won Kim & Zaher Mundher Yaseen & Vijay P Singh, 2020. "Drought index prediction using advanced fuzzy logic model: Regional case study over Kumaon in India," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-31, May.
    4. Tayeb Raziei & Morteza Miri, 2023. "An Alternative Approach for Computing the Standardized Precipitation-Evapotranspiration Index (SPEI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(10), pages 4123-4141, August.
    5. Vahid Moosavi & Mehdi Vafakhah & Bagher Shirmohammadi & Negin Behnia, 2013. "A Wavelet-ANFIS Hybrid Model for Groundwater Level Forecasting for Different Prediction Periods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1301-1321, March.
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    Cited by:

    1. Mohd Imran Khan & Rajib Maity, 2024. "Development of a Long-Range Hydrological Drought Prediction Framework Using Deep Learning," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(4), pages 1497-1509, March.

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