Short-Term Drought Forecast across Two Different Climates Using Machine Learning Models

Abstract

This paper presents a comparative analysis of machine learning (ML) models for predicting drought conditions using the Standardized Precipitation Index (SPI) for two distinct stations, one in Shiraz, Iran and one in Tridolino, Italy. Four ML models, including Artificial Neural Network (ANN), Multiple Linear Regression, K-Nearest Neighbors, and XGBoost Regressor, were employed to forecast multi-scale SPI values (for 6-, 9-, 12-, and 24-month) considering various lag times. Results indicated that the ML model with the most robust performance varied depending on station and SPI duration. Furthermore, ANN demonstrated robust performance for SPI estimations at Shiraz station, whereas no single model consistently outperformed the others for Tridolino station. These findings were further validated through the confidence percentage analysis performed on all ML models in this study. Across all scenarios, longer SPI durations generally yielded better model performance. Additionally, for Shiraz station, optimal lag times varied by SPI duration: 6 months for the 6- and 9-month SPI, 4 months for the 12-month SPI, and 2 months for the 24-month SPI. For Tridolino station, on the other hand, no definitive optimal lag time was identified. These findings contribute to our understanding of predicting drought indicators and supporting effective water resource management and climate change adaptation efforts.

Keywords

• drought
• standardized precipitation index
• machine learning
• XGBoost