Development and implementation of an automatic lahar monitoring system at Misti volcano, Arequipa, Peru
Resumen
In recent decades, the city of Arequipa, Peru, has experienced rapid and largely unplanned urban growth, increasing its exposure to lahars (debris and hyperconcentrated flows) triggered by intense rainfall during the rainy season (December - March). These flows descend through ravine channels from the Misti volcano, causing significant damage and loss of life in suburban areas and the historic center of Arequipa. This context underscores the urgent need for an efficient, real-time, and continuous lahar monitoring system. Here we present the development and implementation of an Automatic Lahars Monitoring System (ALMS) deployed across six ravines that extend from the SW flank of Misti volcano and cross the city of Arequipa. The ALMS is an improved version of the Huaicos Monitoring System (SMH), which has operated since 1990 in the Quebrada Rioseco near Lima, Perú. The ALMS comprises ten autonomous, solar-powered remote stations equipped with radar motion sensors, LiDAR level sensors, surveillance cameras, and machine-learning-based image analysis for continuous monitoring and detection of lahar activity. During the 2024–2025 rainy season, the ALMS successfully detected two lahar events in the Quebrada El Pato and Quebrada Venezuela channels, demonstrating its operational reliability and effectiveness as an early-warning tool. Owing to its low cost, modular design, and robust performance, the ALMS represents a scalable solution that can be replicated in other volcanic and non-volcanic regions to reduce the impacts of debris flows and enhance disaster risk management.
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Palabras clave
Early warning system , Machine learning , Volcanology , Disaster risk reduction
Citación
Espinoza, J. C., Castro, A., Rivera, M., & Vargas, E. (2026). Development and implementation of an automatic lahar monitoring system at Misti volcano, Arequipa, Peru. Journal of South American Earth Sciences, 182 , 106211. https://doi.org/10.1016/j.jsames.2026.106211
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Editor
Elsevier

