470 research outputs found
Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster
Full text linkWe develop a stochastic modeling approach based on spatial point processes of
log-Gaussian Cox type for a collection of around 5000 landslide events provoked
by a precipitation trigger in Sicily, Italy. Through the embedding into a
hierarchical Bayesian estimation framework, we can use the Integrated Nested
Laplace Approximation methodology to make inference and obtain the posterior
estimates. Several mapping units are useful to partition a given study area in
landslide prediction studies. These units hierarchically subdivide the
geographic space from the highest grid-based resolution to the stronger
morphodynamic-oriented slope units. Here we integrate both mapping units into a
single hierarchical model, by treating the landslide triggering locations as a
random point pattern. This approach diverges fundamentally from the unanimously
used presence-absence structure for areal units since we focus on modeling the
expected landslide count jointly within the two mapping units. Predicting this
landslide intensity provides more detailed and complete information as compared
to the classically used susceptibility mapping approach based on relative
probabilities. To illustrate the model's versatility, we compute absolute
probability maps of landslide occurrences and check its predictive power over
space. While the landslide community typically produces spatial predictive
models for landslides only in the sense that covariates are spatially
distributed, no actual spatial dependence has been explicitly integrated so far
for landslide susceptibility. Our novel approach features a spatial latent
effect defined at the slope unit level, allowing us to assess the spatial
influence that remains unexplained by the covariates in the model
OPTIMIZING STOCHASTIC SUSCEPTIBILITY MODELLING FOR DEBRIS FLOW LANDSLIDES: MODEL EXPORTATION, STATISTICAL TECHNIQUES COMPARISON AND USE OF REMOTE SENSING DERIVED PREDICTORS. APPLICATIONS TO THE 2009 MESSINA EVENT.
Get PDFIl presente lavoro di ricerca è stato sviluppato al fine di approfondire approcci metodologici nell'ambito della sucscettibilità da frana. In particolare, il tema centrale della ricerca è rappresentato dal tema specifico dell'esportazione spaziale di modelli di suscettibilità nell'area mediterranea. All'interno del topic specifico dell'esportazione di modelli predittivi spaziali sono state approfondite tematiche relative all'utilizzo di differenti algoritmi o di differenti sorgenti, derivate da DEM o da coperture satellitari.The present work has been developed in order to enhance current methodological approaches within the big picture of the landslide susceptibility. In particular, the central topic was the spatial exportation of landslide susceptibility models within the Mediterranean sector. Within the specific subject pertaining to the spatial exportation of predictive models, different algorithms as well as different data sources have been tested. Data sources experiments assessed the integration of DEM- and remotely sensed- derived parameters in order to improve the landslide prediction
Towards physics-informed neural networks for landslide prediction
Get PDFFor decades, solutions to regional-scale landslide prediction have primarily relied on data-driven models, which, by definition, are disconnected from the physics of the failure mechanism. The success and spread of such tools came from the ability to exploit proxy variables rather than explicit geotechnical ones, as the latter are prohibitive to acquire over broad landscapes. Our work implements a Physics Informed Neural Network (PINN) approach, thereby adding an intermediate constraint to a standard data-driven architecture to solve for the permanent deformation typical of Newmark slope stability methods. This translates into a neural network tasked with explicitly retrieving geotechnical parameters from common proxy variables and then minimizing a loss function with respect to the available coseismic landslide inventory. The results are promising because our model not only produces excellent predictive performance in the form of standard susceptibility output but, in the process, also generates maps of the expected geotechnical properties at a regional scale. Therefore, Such architecture is framed to tackle coseismic landslide prediction, which, if confirmed in other studies, could open up PINN-based near-real-time predictions
Statistics of extremes for natural hazards: landslides and earthquakes
Full text linkIn this chapter, we illustrate the use of split bulk-tail models and
subasymptotic models motivated by extreme-value theory in the context of hazard
assessment for earthquake-induced landslides. A spatial joint areal model is
presented for modeling both landslides counts and landslide sizes, paying
particular attention to extreme landslides, which are the most devastating
ones
Towards an online GIS platform to enhance data and research sharing among meteorologists, natural hazard experts, governments, and the public
Get PDFThe “open era” of climate science is marked by an abundance of datasets across various environmental variables. While there are many evaluation studies, researchers and practitioners often still struggle to select the most suitable dataset or product for their study. The year 2023 marked the hottest year on record, resulting in a series of destructive hazard events, including heatwaves, wildfires, and floods. These conditions underscore the urgent need for enhanced preparedness in disaster risk reduction (DRR). In the field of natural hazards, environmental data are crucial for building more accurate models. We will take 'P' (precipitation) as an example in the presentation, as it's a major trigger for multiple hazards such as floods and landslides. There are dozens of publicly freely available global gridded P products available (including satellite, (re)analysis, gauge, and combinations thereof), but estimates from different products at the same time and location can differ significantly. Currently, there is no effective platform that facilitates the sharing of quantitative information on the relative strengths and weaknesses of these P products between meteorologists and other stakeholders. To address this challenge, we propose the development of a web-based GIS platform which allows users to interactively explore the globe, click on different locations, and access various statistics and databases. Multiple P products and evaluation statistics can be accessed via the platform. We hope this platform will host multiple hazards-related datasets, fostering better collaboration between scientists in the fields of DRR and meteorology. Initially focusing on P data based on our expertise in precipitation and landslide hazard modeling, we aim to expand this resource by involving more scientists from related fields. Additionally, we plan to integrate a ChatGPT-based extension to streamline data access and enhance efficiency for researchers, practitioners, and laypeople. We want to contribute to the collective effort in creating a dynamic, accessible repository of resources and initiatives for the wider geoscience community
Full seismic waveform analysis combined with transformer neural networks improves coseismic landslide prediction
Get PDFSeismic waves can shake mountainous landscapes, triggering thousands of landslides. Regionalscale landslide models primarily rely on shaking intensity parameters obtained by simplifying ground motion time-series into peak scalar values. Such an approach neglects the contribution of ground motion phase and amplitude and their variations over space and time. Here, we address this problem by developing an explainable deep-learning model able to treat the entire wavefield and benchmark it against a model equipped with scalar intensity parameters. The experiments run on the area affected by the 2015Mw7.8 Gorkha, Nepal earthquake reveal a 16% improvement in predictive capacity whenincorporating full waveforms. This improvement is achieved mainly on gentle (~25°) hillslopes exposed to low ground shaking (~0.2 m/s). Moreover, we can largely attribute this improvement to the ground motion before and much after the peak velocity arrival. This underscores the limits of single-intensitymeasures and the untapped potential of full waveform information
At the junction between deep learning and statistics of extremes:Formalizing the landslide hazard definition
Get PDFThe most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are dependent on each other because larger events occur less frequently and vice versa. However, due to the lack of multi-temporal inventories and joint statistical models, modeling such properties via a unified hazard model has always been challenging and has yet to be attempted. Here, we develop a unified model to estimate landslide hazard at the slope unit level to address such gaps. We employed deep learning, combined with extreme-value theory to analyze an inventory of 30 years of observed rainfall-triggered landslides in Nepal and assess landslide hazard for multiple return periods. We also use our model to further explore landslide hazard for the same return periods under different climate change scenarios up to the end of the century. Our model performs excellently (with an accuracy of 0.78 and an area under the curve of 0.86) and can be used to model landslide hazard in a unified manner. Geomorphologically, we find that under climate change scenarios (SSP245 and SSP585), landslide hazard is likely to increase up to two times on average in the lower Himalayan regions (Siwalik and lower Himalayas; ≈110–3,500 m) while remaining the same in the middle Himalayan region (≈3,500–5,000 m) whilst decreasing slightly in the upper Himalayan region (≳5,000 m) areas
Zero viscosity limit of the Oseen equations in a channel
Get PDFOseen equations in the channel are considered. We give an explicit solution formula in terms of the inverse heat operators and of projection operators. This solution formula is used for the analysis of the behavior of the Oseen equations in the zero viscosity limit. We prove that the solution of Oseen equations converges in W1,2 to the solution of the linearized Euler equations outside the boundary layer and to the solution of the linearized Prandtl equations inside the boundary layer. © 2001 Society for Industrial and Applied Mathematics
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