Revista de Investigaciones Altoandinas - Journal of High Andean Research

Vol. 28 (2026): Publicación continua
Original articles

Meteorological Frost Prediction Using Ensemble Machine Learning Models With a Multi-Horizon Approach for Agroclimatic Early Warning in the Puno Altiplano, 2015–2024

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Romel Gonzalo Quispe-Valero
Departamento de Estadística e Informática, Facultad de Ingeniería Estadística e Informática, Universidad Nacional del Altiplano, Puno, Perú
Jorge Luis Flores-Quispe
Universidad Nacional del Altiplano, Puno, Perú

Published 2026-08-28

Keywords

  • machine learning,
  • weather forecasting,
  • frost,
  • Peru,
  • database

Copyright (c) 2026 Romel Gonzalo Quispe Valero

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Quispe-Valero, R. G., & Flores-Quispe, J. L. (2026). Meteorological Frost Prediction Using Ensemble Machine Learning Models With a Multi-Horizon Approach for Agroclimatic Early Warning in the Puno Altiplano, 2015–2024. Revista De Investigaciones Altoandinas - Journal of High Andean Research, 28, e28849. https://doi.org/10.18271/ria.2026.849

Abstract

The predictive capacity of nine machine learning algorithms was evaluated for forecasting meteorological frosts at six weather stations in the Puno region (Peru) across three different time horizons (24, 48, and 72 h). In the second step, climate reanalysis data (ERA5-Land using the Open-Meteo API) from the period 2015–2024 were consolidated into 526,032 hourly records corresponding to 21,918 daily observations from up to 57 agroclimatic predictors. Frosts showed a regional average prevalence of 17.4%, exhibiting marked spatial heterogeneity ranging from 31.3% in Ilave to 7.7% in Puno, highlighting the regulatory role of Lake Titicaca’s temperature. A five-fold stratified cross-validation was performed to estimate the AUC-ROC, F1-score, accuracy, sensitivity, and Cohen’s kappa for model performance. The best overall performance was obtained using the voting ensemble, with an AUC-ROC of 0.957 and an F1-score of 0.744 at 24 h, corresponding to values of 0.913 and 0.656 at a 72 h cutoff time, respectively. Threshold optimization led to a regional F1-score improvement of +0.025 compared to the conventional threshold (t = 0.50). SHAP value analysis revealed that the driver with the greatest impact was the 3-day moving average minimum temperature (mean |SHAP| = 1.274). The developed model represents an objective early agroclimatic warning tool for crops in the Andean highlands.

pdf (Spanish)

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