Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques

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,...

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Bibliographic Details
Published in:Water resources management 2023-12, Vol.37 (15), p.6051-6070
Main Authors: Jariwala, K. A., Agnihotri, P. G.
Format: Article
Language:English
Subjects:
Arid regions
Arid zones
Atmospheric models
Atmospheric Sciences
Civil Engineering
Comparative analysis
Drought
Earth and Environmental Science
Earth Sciences
Environment
Forecasting
Geotechnical Engineering & Applied Earth Sciences
History
Hydrogeology
Hydrology/Water Resources
issues and policy
Learning algorithms
Machine learning
Mathematical models
Modelling
Performance prediction
Resource allocation
risk assessment
Soft computing
Statistical analysis
Statistical methods
Statistical models
water
Water management
Water scarcity
water shortages
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Summary: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.
ISSN:0920-4741
1573-1650
DOI:10.1007/s11269-023-03642-6