Optimized 3D rainfall thresholds: false alarms reduction and multi-source validation

A regional-scale landslide forecasting model based on rainfall thresholds was optimized for operational early warning. In particular, we addressed two main issues that usually hinder the operational implementation of this kind of models: (i) the excessive number of false alarms, resulting in civil protection system activation without any real need, and (ii) the validation procedure, usually performed over periods too short to guarantee model reliability.

To overcome these limitations, several techniques for reducing the number of false alarms were applied in this study, and a multiple validation phase was conducted using data from different sources. An intensity-duration threshold system for each of the five alert zones composing the Liguria region (Italy) was identified using a semiautomatic procedure called MaCumBA, considering three levels of criticality: low, moderate, and high. The thresholds were developed using a landslide inventory collected from online newspapers by a data mining technique called SECaGN. This method was chosen to account for only those events that echo on the Internet and therefore impact society, ignoring landslides occurred in remote areas, not of interest for civil protection intervention and resulting in false alarms. A calibration phase was performed to minimize the impact of false alarms, allowing at least one false alarm per year over the moderate criticality level. A novel datset containing only very severe disasters that required national-level emergencies was used to calibrate the high criticality threshold. In addition, we applied an innovative approach to include an antecedent rainfall indicator as third variable. The threshold is thus not represented by a traditional line in a 2D spce, but by a plane in a 3D space.

This approach allowed for a consistent reduction in false alarms. The results were validated through an independent landslide inventory and were compared with (i) the alert issued by the regional civil protection agency to observe the improvements achieved with the proposed model and to evaluate to what extent the proposed model is consistent with the assessments of the civil protection and (ii) a dataset of the national states of emergency to verify the suitability of the developed thresholds for alerting citizens. The 3D thresholds showed high predictive capabilities, confirming their suitability for implementation in an operational landslide early warning system.