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

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Geoscience Frontiers 2024年第5期15卷 75-92页

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 GeoSphere Austria ViennaAustria Center for Climate Change and Transformation Eurac ResearchBolzanoItaly Faculty of Geo-information Science and Earth Observation(ITC) University of TwenteEnschedethe Netherlands Research Institute for Geo-Hydrological Protection National Research Council(CNR-IRPI)PerugiaItaly Department of Geosciences University of PaduaPaduaItaly Institute of Atmospheric Sciences and Climate National Research Council(CNR-ISAC)BolognaItaly Department of Geography University of InnsbruckInnsbruckAustria Department of Natural Hazards Austrian Research Centre for Forests(BFW)InnsbruckAustria Office for Geology and Building Materials Testing Autonomous Province of Bolzano-South TyrolCardanoItaly

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition *** 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 *** 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 *** 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 *** 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 *** 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 *** setup entails integrating landslide controls that operate on various temporal scales through a binomial generalized Additive Mixed *** relationships are then interpreted based on variable importance and partial effect plots,while predictive performance is evaluated through various crossvalidation *** 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 *** model demonstrates high predictive performanc

关键词： Early warning Space-time model Rainfall thresholds landslide susceptibility,generalized Additive Mixed Model Forecasting

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