Using the ARIMA/SARIMA Model for Afghanistan's Drought Forecasting Based on Standardized Precipitation Index

Abstract

Forecasting drought plays a vital role in strategic planning and the management of underground water supply. In this study, we utilized autoregressive integrated moving average (ARIMA) and Seasonal ARIMA (SARIMA) models to predict drought events in Afghanistan, based on the standardized precipitation index (SPI). We used monthly average precipitation data from 1991 to 2015 for model training, while data from 2016 to 2020 were employed for model validation. The results of the statistical analysis, which  encompassed evaluating Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE), indicated that among the SPI 3, SPI 6, SPI 9, SPI 12, and SPI 24, the SARIMA models applied to the SPI 24 demonstrated the most accurate forecasting performance with RMSE (0.1492), MAE (0.1039), and MAPE (22.3732%) compared to SPI 3, SPI 6, SPI 9, and SPI 12. Subsequently, the ARIMA/SARIMA models were employed to forecast drought events for the upcoming year. It’s noteworthy that this constitutes the first-ever statistical analysis of the drought index in Afghanistan. Therefore, the outcomes of this study can be applied across diverse sectors, including water resource management and environmental precautions.