Drought forecasting using the Prophet model in a semi-arid climate region of western India

Accurate drought forecasting is necessary for early warning of drought hazards, water resources and eco-environmental management. This study assessed the capability of the Prophet model to forecast meteorological drought based on the standardized precipitation index (SPI). A comparative assessment was conducted among Prophet, support vector regression (SVR) and multiple linear regression (MLR) models to test the applicability of the new model. The SPI was computed at multiple time scales (SPI3, SPI6, SPI12 and SPI24) for 38 meteorological stations located in semi-arid areas in western India. Minimum redundancy maximum relevance (mRMR) was applied to select input variables prior to the model development. The results reveal that the Prophet model yielded acceptable accuracy for drought forecasting, whereas SVR and MLR models showed greater error for short-term forecasting in terms of coefficient of determination (R-2) and Nash-Sutcliffe efficiency (NSE). Therefore, the Prophet model is recommended as a new robust model for drought forecasting.

Automated summary

Accurate drought forecasting is crucial for early warning and management of drought hazards. This study evaluated the performance of the Prophet model in meteorological drought forecasting and found that it outperformed traditional SVR and MLR models, making it a recommended robust model for drought forecasting.