Prediction rate functions of landslide susceptibility applied in the Iberian Peninsula

摘要

The prediction models of landslide susceptibility that we have developed, generate not only predicted hazard maps but also prediction-rate curves, which allow us to estimate the probabilities of the occurrences of future landslides from the hazard maps. For a risk-analysis estimating the "economic" values of the population, properties, economic activities, etc., the estimates of the probabilities are absolutely critical statistics. To use the hazard maps for risk analysis, we must be able to estimate the probability of occurrence of a future landslide at each hazard level in the maps. Without the estimates of such probabilities, the hazard maps can provide only indicators of landslide hazard, but they cannot be directly useful for a decision process. With those probabilities, however, decision makers can quantitatively assess the economic sterilization due to the possible damage under the assumptions of appropriate scenarios. Hence, they can take a learned and informed decision. Predictions are based on "Favourability Functions" that integrate the spatial relationships between the distribution of trigger zones of specific dynamic types of landslides and the surrounding mapping units and contour intervals. The latter two represent the spatial support to estimate the likelihood of further failures. The prediction-rate curves are obtained to interpret the predictions in spatial-temporal terms by partitioning the database of trigger-zone distribution and spatial-support layers (digital maps) in periods of activity, or in non-overlapping spatial sub-sets, needed for prediction and validation, respectively. The shapes of the prediction-rate curves empirically measure the quality of the predictions, their robustness, and provide the arguments for the cost-benefit analysis of the relationships between the spatial prediction patterns and the prediction-rate curves. Two applications of Favourability Function models in the Iberian Peninsula are used to demonstrate the operational feasibility of generating the prediction-rate curves for the spatial-temporal validation needed for risk assessment.