| 21 Aug 2026
ALERT: A Scalable Cloud-Native Framework for Satellite Rainfall-Driven Landslide Early Warning in Data-Scarce Regions
Abstract. Rainfall-triggered landslides cause thousands of fatalities each year, yet effective early warning remains challenging across many mountainous regions because of sparse rainfall observations and limited forecasting infrastructure. Here we present ALERT (Automated Landslide Early Risk Tracker), a scalable, cloud-native framework that integrates NASA IMERG satellite precipitation, ECMWF operational forecasts, terrain susceptibility, rainfall intensity–duration thresholds, debris-flow runout modelling, and building exposure to deliver near-real-time, impact-based landslide warnings. ALERT incorporates a globally transferable catalogue of 24 rainfall thresholds while allowing user-defined thresholds and susceptibility layers, enabling deployment across diverse climatic and geomorphological settings. Event-based validation achieved a sensitivity of 0.80 and an overall accuracy exceeding 0.70, demonstrating effective detection of hazardous rainfall conditions for landslide initiation. By reducing dependence on dense ground-based rain-gauge networks, ALERT provides a computationally efficient and scalable pathway towards regional scale landslide early warning and climate-resilient disaster risk reduction, particularly in data-scarce regions.
This is an interesting and potentially useful framework, but the manuscript currently overstates its performance. In its present form, ALERT is a promising web application, not a validated operational landslide early-warning system.
Here, my main concerns are:
There are also several inconsistencies: accuracy is exactly 0.70, not exceeding 0.70; 5,474 + 7,707 equals 13,181, not 13,188; the abstract reports 24 thresholds whereas the manuscript reports 23; and Figure 9 contains only 65 landslide samples despite stating that 104 were available.