Double-Threshold Validation Tool (DTVT): a tool for automatically validating and converting pixel-based landslide hazard maps into actionable warning criteria

Landslide Early Warning Systems (LEWSs) are cost-effective solutions designed to prevent loss of life and economic damage caused by landslides by issuing timely warnings to communities. Traditionally, LEWSs rely on rainfall thresholds, which, while simple and accessible, consider only rainfall data and overlook critical hydrogeological soil properties. To improve accuracy, Machine Learning (ML) algorithms have been adopted to generate landslide susceptibility maps by integrating multiple geoenvironmental factors. However, susceptibility maps lack a temporal dimension, limiting their applicability to LEWSs.

Recent advancements in ML have enabled the creation of Landslide Hazard Maps (LHMs) that incorporate spatial and temporal predictions, significantly enhancing their relevance for LEWSs. Despite these improvements, their practical implementation into LEWSs faces two challenges: i) the absence of standardised validation methods to ensure reliability, and ii) a mismatch between pixel-based LHMs and the larger spatial units used for regional warnings. This discrepancy limits the use of LHMs by civil protection authorities, who require simplified and reliable data to effectively coordinate warnings and responses over wide areas.

This study introduces a standardised and automatic validation approach using the Double-Threshold Validation Tool (DTVT). This tool aggregates pixel-based LHMs into broader spatial units called Pixel Aggregation Units (PAUs). Each PAU is classified as unstable based on two thresholds: the Failure Probability Threshold (FPT), indicating the probability above which a pixel is considered unstable, and the Instability Diffusion Threshold (IDT), defining the minimum number of unstable pixels required to classify an entire PAU as unstable.

The DTVT automatically iterates through FPT-IDT combinations, calculating performance metrics to identify the optimal pair that ensures zero missed alarms and minimizes false positives. This process transforms detailed, pixel-based maps into practical hazard assessments suitable for regional LEWSs. Furthermore, the DTVT allows for the calibration of three criticality levels (low, moderate, and high) by adjusting FPT and IDT values.

To demonstrate its effectiveness, the study applies the DTVT in Florence, Italy, using LHMs developed with advanced ML techniques incorporating temporal dimensions. The case study illustrates how the DTVT simplifies complex landslide probability data into actionable warnings, enabling real-time decisions by civil protection agencies.