Comparative Analysis of Forecasting Strategies for Meteorological Droughts: From Classical Statistics to Artificial Intelligence Algorithms
DOI:
https://doi.org/10.70577/0dpbb154Keywords:
Meteorological droughts, Standardized Precipitation Index (SPI), Drought forecasting, Machine learning, Forecasting models, Paute River basin, Data scarcity.Abstract
The forecasting of meteorological droughts in tropical mountain basins is limited by orographic complexity, climate variability, and data scarcity. This study compares five forecasting models (ARIMA/SARIMA, ETS, Random Forest, XGBoost, and SVM-RBF) applied to the Standardized Precipitation Index (SPI) at 1, 3, 6, and 12-month timescales, using monthly precipitation records (1970–2023) from two stations in the upper Paute River basin in southern Ecuador.
The models were calibrated and validated using an 80/20 training-test split and evaluated with different performance metrics and statistical tests. The results indicate that the timescale of the SPI was the main factor influencing the predictive performance of the models. Machine learning models outperformed classical statistical models from SPI-3 to SPI-12; however, in moving forecast assessments in SPI-12, the five models showed no significant differences. Model complexity did not guarantee improved forecasting, highlighting the usefulness of parsimonious models for operational drought monitoring in Andean basins, which face data scarcity.
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