Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models

The rising frequency and severity of droughts requires accurate monitoring and forecasting to reduce the impact on water resources and communities. This study aims to investigate drought monitoring and categorization, while enhancing drought forecasting by using three machine learning models—Artificial Neural Network (ANN), Support Vector Machine (SVM), and Random Forest (RF). The models were trained on the study region’s historic precipitation and temperature data (minimum and maximum) from 1960 to 2021. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) were computed for a time scale of 3, 6 and 12 months. The monthly precipitation data were used for creating lag scenarios and were used as input features for the models to improve the models’ performance and reduce overfitting. Statistical parameters like the coefficient of determination (R2), Mean Absolute Error (MAE), Root mean square error (RMSE) and Nash–Sutcliffe Efficiency (NSE) were determined to evaluate the model accuracy. For forecasting, the SPEI3, ANN and SVM models show better performance (R2 > 0.9) than the RF models when the 3-month lag data were used as input features. For SPEI6 and SPEI12, the 6-month lag and 12-month lag data, respectively, were needed to increase the models’ accuracy. The models exhibited RMSE values of 0.27 for ANN, 0.28 for SVM, and 0.37 for RF for the SPEI3, indicating the superior performance of the former two. The models’ accuracy increases as the lag period increases for SPI forecasting. Overall, the ANN and SVM models outperformed the RF model for forecasting long-term drought.