A machine learning model for drought tracking and forecasting using remote precipitation data and a standardized precipitation index from arid regions

Drought is a catastrophe that impacts agriculture and causes economic and social damage. An effective monitoring and forecasting system is needed to assess the extent of droughts and to mitigate their effects at both spatial and temporal levels. To this end, we used a Standardized Precipitation Index (SPI) in various timescales to classify and track drought events based on CHIRPS rainfall data for the period between 1981 and 2019. Three models (M1, M2, M3) were then tested for annual drought prediction (SPI_12) using precipitation data and the lagged SPI as input variables. Extreme Learning Machine algorithms displayed rapid drought prediction, with high accuracy on different timescales (0.7-0.8 R-2).

Automated summary

This study used the Standardized Precipitation Index (SPI) to classify and track drought events from 1981 to 2019, aiming to establish an effective monitoring and forecasting system.