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- 저자 : Filippo Catani (검색결과 5 건)
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Geotechnical and Hydrological Data Assimilation in Numerical Slope Stability Models
( Filippo Catani ),( Elena Benedetta Masi ),( Guglielmo Rossi ),( Gabriele Bicocchi ),( Veronica Tofani ) 대한지질공학회 2019 대한지질공학회 학술발표회논문집 Vol.2019 No.2
Numerical models of slope stability may be applied to large areas in a way that is useful for early warning applications provided that some basic requirements are met. Among them, very important is the correct assimilation of soil mechanical and hydrological parameters, such as saturated hydraulic conductivity, cohesion and friction angle. Almost every single numerical parameter used in the numerical models shows a strong variability in space and cannot be directly measured at the same resolution of the finite element model. Therefore, an averaging and interpolation scheme must be devised which relies on the field measurements and on some statistical analysis to determine suitable probability distributions to be used in Monte Carlo analysis. In this presentation we report on the application of a complete methodology for field and laboratory testing of geo-hydrological parameters, their statistical analysis in terms of probability distributions and data assimilation into the High Resolution Slope Stability Simulator (Rossi et al., 2013) with the aim of implementing a near-real-time application of an early warning system for shallow landslides induced by rainfall in the region of Tuscany, Italy. Statistics on the main model parameters are presented and discussed as related to the main soil types found in the region, including grain size, mineralogical composition, shear strength, matric suction and hydraulic conductivity. Some preliminary data on vegetation effects are also presented, with reference to root effects and apparent root cohesion implementation in the numerical model. Some simulation examples are also presented.
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Landslide prediction, monitoring and early warning: a concise review of state-of-the-art
채병곤,박혁진,Filippo Catani,Alessandro Simoni,Matteo Berti 한국지질과학협의회 2017 Geosciences Journal Vol.21 No.6
Landslide is one of the repeated geological hazards during rainy season, which causes fatalities, damage to property and economic losses in Korea. Landslides are responsible for at least 17% of all fatalities from natural hazards worldwide, and nearly 25% of annual casualties caused by natural hazards in Korea. Due to global climate change, the frequency of landslide occurrence has been increased and subsequently, the losses and damages associated with landslides also have been increased. Therefore, accurate prediction of landslide occurrence, and monitoring and early warning for ground movements are very important tasks to reduce the damages and losses caused by landslides. Various studies on landslide prediction and reduction in landslide damage have been performed and consequently, much of the recent progress has been in these areas. In particular, the application of information and geospatial technologies such as remote sensing and geographic information systems (GIS) has greatly contributed to landslide hazard assessment studies over recent years. In this paper, the recent advances and the state-of-the-art in the essential components of the landslide hazard assessment, such as landslide susceptibility analysis, runout modeling, landslide monitoring and early warning, were reviewed. Especially, this paper focused on the evaluation of the landslide susceptibility using probabilistic approach and physically based method, runout evaluation using volume based model and dynamic model, in situ ground based monitoring techniques, remote sensing techniques for landslide monitoring, and landslide early warning using rainfall and physical thresholds.
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Faming Huang,Haowen Xiong,Xiaoting Zhou,Filippo Catani,Jinsong Huang 대한토목학회 2024 KSCE Journal of Civil Engineering Vol.28 No.1
The utilization of different connection methods between landslides and environmental factors introduces uncertainty in landslide susceptibility prediction (LSP). Investigating and identifying the characteristics of this uncertainty and determining more suitable connection methods are of significant importance for LSP. This study uses original 12 environmental factors data as well as calculated data from five connection methods, namely, probability statistics (PS), frequency ratio (FR), information volume (IV), index of entropy (IOE), and weight of evidence (WOE), as model input variables. Then, four machine learnings logistic regression (LR), Bayesian networks (BN), support vector machines (SVM) and C5.0 Decision Trees (C5.0 DT) are combined with the calculated data and the original data to create 24 unique combinations of connection methods and models. Under these 24 combinations, the uncertainty analysis of LSP modeling is conducted, using Huichang County of China as an example. The analysis entails accuracy assessment, statistical analysis of landslide susceptibility indexes (LSIs), distribution patterns of LSIs and sensitivity analysis of the two uncertainty issues. The results show that: 1) LSP accuracies of the FR-, IV- and IOE-based models are comparable, but are lower than those of the WOE-based models, with those of the PS-based models being the worst. WOE has better nonlinear connection performance than the other methods. 2) LSP accuracies of individual models are slightly lower than those of coupled models, but their modeling efficiencies are higher than those of coupled models. 3) The machine learning is more sensitive than the connection method for LSP. In short, WOE-C5.0 DT has the lowest LSP uncertainty while a single machine learning can produce satisfied LSP results with high modelling efficiency.
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