Forecasting of SPI and Meteorological Drought Based on the Artificial Neural Network and M5P Model Tree

Climate change has caused droughts to increase in frequency and severity worldwide, which attracted scientists create drought prediction models mitigate the impacts of droughts. One most important challenges addressing is developing accurate predict their discrete characteristics, i.e., occurrence, duration, severity. The current research examined performance several different machine learning models, including Artificial Neural Network (ANN) M5P Tree forecasting widely used measure, Standardized Precipitation Index (SPI), at both time scales (SPI 3, SPI 6). model was developed utilizing rainfall data from two stations India (i.e., Angangaon Dahalewadi) for 2000–2019, wherein first 14 years are employed training, while remaining six validation. subset regression analysis performed on 12 input combinations choose best combination 3 6. sensitivity carried out given find effective parameter forecasting. all ANN (4, 5), (5, 6), (6, 7), assessed through statistical indicators, namely, MAE, RMSE, RAE, RRSE, r. results revealed that (t-1) sensitive parameters with highest values ? = 0.916, 1.017, respectively, SPI-3 SPI-6 7 (SPI-1/SPI-3/SPI-4/SPI-5/SPI-8/SPI-9/SPI-11) 4 (SPI-1/SPI-2/SPI-6/SPI-7) based higher R2 Adjusted lowest MSE values. It clear r lesser RMSE as compared 7) models. Therefore, superior other stations.