Adopting the margin of stability for space–time landslide prediction–A data-driven approach for generating spatial dynamic thresholds  

作　　者：Stefan Steger Mateo Moreno Alice Crespi Stefano Luigi Gariano Maria Teresa Brunetti Massimo Melillo Silvia Peruccacci Francesco Marra Lotte de Vugt Thomas Zieher Martin Rutzinger Volkmar Mair Massimiliano Pittore 

机构地区：[1]GeoSphere Austria,Vienna,Austria [2]Center for Climate Change and Transformation,Eurac Research,Bolzano,Italy [3]Faculty of Geo-information Science and Earth Observation(ITC),University of Twente,Enschede,the Netherlands [4]Research Institute for Geo-Hydrological Protection,National Research Council(CNR-IRPI),Perugia,Italy [5]Department of Geosciences,University of Padua,Padua,Italy [6]Institute of Atmospheric Sciences and Climate,National Research Council(CNR-ISAC),Bologna,Italy [7]Department of Geography,University of Innsbruck,Innsbruck,Austria [8]Department of Natural Hazards,Austrian Research Centre for Forests(BFW),Innsbruck,Austria [9]Office for Geology and Building Materials Testing,Autonomous Province of Bolzano-South Tyrol,Cardano,Italy

出　　处：《Geoscience Frontiers》2024年第5期75-92,共18页地学前缘（英文版）

基　　金：The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano-Südtirol/Alto Adige.

摘　　要：Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors.While data-driven methods for assessing landslide susceptibility or for establishing rainfall-triggering thresholds are prevalent,integrating spatiotemporal information for dynamic large-area landslide prediction remains a challenge.The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data.Unlike previous studies focusing on space–time landslide modelling,it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results,while ensuring interpretable outcomes.It introduces also other noteworthy innovations,such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario-building.The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol,Italy(7400 km2)within well-investigated terrain.Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model.Model relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation techniques.Optimal and user-defined probability cutpoints are used to establish quantitative thresholds that reflect both,the true positive rate(correctly predicted landslides)and the false positive rate(precipitation periods misclassified as landslide-inducing conditions).The resulting dynamic maps directly visualize landslide threshold exceedance.The model demonstrates high predictive performance while revealing geomorphologically plausible

关 键 词：Early warning Space-time model Rainfall thresholds Landslide susceptibility Generalized Additive Mixed Model Forecasting 

分 类 号：P642.22[天文地球—工程地质学]