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.
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The datasets generated and analyzed during the present study will be available upon reasonable request.
Abbreviations
- SPI:
-
Standard Precipitation Index
- ANFIS:
-
An adaptive neuro-fuzzy inference system
- ARIMAX:
-
Auto-Regressive Integrated Moving Average with Exogeneous variable
- ADF:
-
Augmented Dickey Fuller Test
- ACF:
-
Autocorrelation Function
- PACF:
-
Partial Autocorrelation Function
- GIS:
-
Geographic information system
- RS:
-
Remote Sensing
- MSE:
-
Mean squared error
- RMSE:
-
Root mean squared error
- ANN:
-
Artificial Neural Network
- ARIMA:
-
Auto-Regressive Integrated Moving Average
- CMIP 6:
-
Coupled Model Intercomparison Project Phase 6
- MF:
-
Membership Function
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Acknowledgements
The authors gratefully acknowledge the support provided by the Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat, India, that enabled the completion of this research. We sincerely thank the Worldclim CRU data for providing the required meteorological data for drought modeling. Special thanks are also due to our dedicated team of survey participants, whose contributions were essential for gathering meaningful data.
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Both authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [K. A. Jariwala]. The initial review was conducted by [P. G. Agnihotri]. Both authors read and approved the final manuscript.
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Jariwala, K.A., Agnihotri, P.G. Comparative Analysis of Drought Modeling and Forecasting Using Soft Computing Techniques. Water Resour Manage 37, 6051–6070 (2023). https://doi.org/10.1007/s11269-023-03642-6
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DOI: https://doi.org/10.1007/s11269-023-03642-6