Drought prediction in high-Andean areas of Puno using a hybrid LSTM–XGBoost model
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Abstract
Droughts are one of the main climatic risks for high-Andean agriculture in Puno, affecting the production of quinoa, native potato and livestock pastures. The objective of this study was to evaluate a hybrid model combining LSTM networks with XGBoost to predict drought events using open meteorological data. The methodology used daily series for the period 2000–2025 from the NASA POWER platform for twelve points in Puno, from which the Standardized Precipitation Index (SPI) was computed and drought was defined as SPI below −1.0. The model was trained on 80 % of the data and tested on the remaining 20 % using a chronological split that prevents temporal data leakage, with accuracy, precision, recall, F1-score and AUC-ROC. Results showed 95.97 % accuracy (95 % CI: 94.3–97.2 %) and an AUC-ROC of 0.9694, with a recall of 0.9035 for the drought class; the approaches with temporal memory (the LSTM and the hybrid) clearly outperformed logistic regression and XGBoost applied without temporal dynamics. The hybrid model is concluded to be a viable support tool for drought early-warning systems in high-Andean areas.
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