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 R²).

干旱是一场影响农业并造成经济和社会破坏的灾难。需要一个有效的监测和预报系统来评估干旱的程度并减轻其在时空上的影响。为此，我们基于1981年至2019年期间的CHIRPS降雨数据，在各个时间尺度上使用了标准化降水指数（SPI）对干旱事件进行分类和跟踪。然后，对三种模型（M1，M2，M3）进行了年度干旱测试使用降水量数据和滞后SPI作为输入变量进行预测（SPI_12）。极限学习机算法显示了快速的干旱预测，在不同的时间尺度（0.7–0.8 R ²）上具有很高的准确性。