方法对比

	Landslide Susceptibility Mapping	Tsunami Inundation Modeling
领域	Disaster Studies	Disaster Studies
方法族	Process / pipeline	Process / pipeline
起源年份	2006	1998
提出者	Fausto Guzzetti, Paola Reichenbach and colleagues (CNR-IRPI; statistical landslide susceptibility tradition)	Vasily Titov & Costas Synolakis (MOST model and benchmarking)
类型	Spatial statistical classification pipeline over mapping units	Shallow-water numerical simulation pipeline (generation-propagation-inundation)
开创性文献	Reichenbach, P., Rossi, M., Malamud, B. D., Mihir, M., & Guzzetti, F. (2018). A review of statistically-based landslide susceptibility models. Earth-Science Reviews, 180, 60-91.	Titov, V. V., & Synolakis, C. E. (1998). Numerical Modeling of Tidal Wave Runup. Journal of Waterway, Port, Coastal, and Ocean Engineering, 124(4), 157-171.
别名	Landslide Susceptibility Modeling, Slope-Failure Susceptibility Mapping, Statistical Landslide Hazard Mapping, Landslide Probability Mapping	Tsunami Runup Modeling, Tsunami Flooding Simulation, Shallow-Water Tsunami Inundation, Tsunami Hazard Simulation
相关	3	3
摘要	Landslide susceptibility mapping estimates where slope failures are likely to occur by statistically relating a mapped inventory of past landslides to the terrain conditions that predispose a slope to fail. The premise, articulated across the statistical landslide literature that Guzzetti, Reichenbach, and colleagues helped systematize, is that landslides recur under geological and morphological conditions similar to those that produced them before, so the spatial pattern of past failures reveals the susceptibility of as-yet unfailed terrain. The analyst partitions the landscape into mapping units, characterizes each by conditioning factors such as slope, aspect, lithology, and land cover, and fits a classifier — logistic regression, discriminant analysis, or machine learning — to predict the probability of failure. Reichenbach and co-authors' 2018 review of 565 studies catalogued the methods, factors, and validation practices, while Guzzetti and co-workers' 2006 paper established how to rigorously assess model quality. The output is a zonation ranking terrain from low to high susceptibility. Susceptibility maps describe spatial likelihood, not when or how large a failure will be.	Tsunami inundation modeling simulates the entire life of a tsunami — its generation by seafloor displacement, its propagation across the ocean, and its runup and flooding of the coast — by numerically solving the equations of shallow-water hydrodynamics. The shallow-water approximation holds because a tsunami's wavelength vastly exceeds the ocean depth, so the wave behaves as a long wave whose speed depends on water depth, refracting and shoaling as it approaches shore. Titov and Synolakis's 1998 work on numerical modeling of long-wave runup established the Method of Splitting Tsunami (MOST), a finite-difference solver that became the operational standard for tsunami forecasting and inundation mapping. Because such models drive emergency planning, Synolakis and colleagues' 2008 paper set out the analytical, laboratory, and field benchmarks every tsunami model must pass to be trusted. The defining technical challenge is the moving shoreline — the wetting and drying of land as the wave advances and retreats. The output is a map of maximum inundation depth, extent, and runup elevation along the coast.