scholarly journals THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 105-107 ◽  
Author(s):  
W. Y. Li ◽  
C. Liu ◽  
J. Gao
Keyword(s):  
Hot Spot ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Physical Models ◽  
Slope Instability ◽  
Hazard Evaluation ◽  
Landslide Risk ◽  
Physically Based Model ◽  
Multi Scale ◽  
Physically Based

Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

scholarly journals THE FRAMEWORK ON MULTI-SCALE LANDSLIDE HAZARD EVALUATION IN CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 105-107 ◽  
Author(s):  
W. Y. Li ◽  
C. Liu ◽  
J. Gao
Keyword(s):  
Hot Spot ◽  
Regional Scale ◽  
Landslide Hazard ◽  
Physical Models ◽  
Slope Instability ◽  
Hazard Evaluation ◽  
Landslide Risk ◽  
Physically Based Model ◽  
Multi Scale ◽  
Physically Based

Nowadays, Landslide has been one of the most frequent and seriously widespread natural hazards all over the world. How landslides can be monitored and predicted is an urgent research topic of the international landslide research community. Particularly, there is a lack of high quality and updated landslide risk maps and guidelines that can be employed to better mitigate and prevent landslide disasters in many emerging regions, including China. This paper considers national and regional scale, and introduces the framework on combining the empirical and physical models for landslide evaluation. Firstly, landslide susceptibility in national scale is mapped based on empirical model, and indicates the hot-spot areas. Secondly, the physically based model can indicate the process of slope instability in the hot-spot areas. The result proves that the framework is a systematic method on landslide hazard monitoring and early warning.

Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy

Geomorphology ◽  
1999 ◽  
Vol 31 (1-4) ◽  
pp. 181-216 ◽  
Author(s):  
Fausto Guzzetti ◽  
Alberto Carrara ◽  
Mauro Cardinali ◽  
Paola Reichenbach
Keyword(s):  
Central Italy ◽  
Landslide Hazard ◽  
Hazard Evaluation ◽  
Multi Scale

scholarly journals Towards a real-time susceptibility assessment of rainfall-induced shallow landslides on a regional scale

2011 ◽  
Vol 11 (7) ◽  
pp. 1927-1947 ◽  
Author(s):  
L. Montrasio ◽  
R. Valentino ◽  
G. L. Losi
Keyword(s):  
Real Time ◽  
Regional Scale ◽  
Back Analysis ◽  
Early Warning Systems ◽  
Shallow Landslides ◽  
Landslide Risk ◽  
Time Interval ◽  
Slip Model ◽  
Temporal Prediction ◽  
Physically Based

Abstract. In the framework of landslide risk management, it appears relevant to assess, both in space and in time, the triggering of rainfall-induced shallow landslides, in order to prevent damages due to these kind of disasters. In this context, the use of real-time landslide early warning systems has been attracting more and more attention from the scientific community. This paper deals with the application, on a regional scale, of two physically-based stability models: SLIP (Shallow Landslides Instability Prediction) and TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope-stability analysis). A back analysis of some recent case-histories of soil slips which occurred in the territory of the central Emilian Apennine, Emilia Romagna Region (Northern Italy) is carried out and the main results are shown. The study area is described from geological and climatic viewpoints. The acquisition of geospatial information regarding the topography, the soil properties and the local landslide inventory is also explained. The paper outlines the main features of the SLIP model and the basic assumptions of TRIGRS. Particular attention is devoted to the discussion of the input data, which have been stored and managed through a Geographic Information System (GIS) platform. Results of the SLIP model on a regional scale, over a one year time interval, are finally presented. The results predicted by the SLIP model are analysed both in terms of safety factor (Fs) maps, corresponding to particular rainfall events, and in terms of time-varying percentage of unstable areas over the considered time interval. The paper compares observed landslide localizations with those predicted by the SLIP model. A further quantitative comparison between SLIP and TRIGRS, both applied to the most important event occurred during the analysed period, is presented. The limits of the SLIP model, mainly due to some restrictions of simplifying the physically based relationships, are analysed in detail. Although an improvement, in terms of spatial accuracy, is needed, thanks to the fast calculation and the satisfactory temporal prediction of landslides, the SLIP model applied on the study area shows certain potential as a landslides forecasting tool on a regional scale.

scholarly journals Assessment of Landslide Hazard in Jiangxi Using Geo-information Technology

2021 ◽  
Vol 9 ◽  
Author(s):  
Penghui Ou ◽  
Weicheng Wu ◽  
Yaozu Qin ◽  
Xiaoting Zhou ◽  
Wenchao Huangfu ◽  
...  
Keyword(s):  
Regional Scale ◽  
Binary Logistic Regression ◽  
Technical Support ◽  
Landslide Hazard ◽  
Information Value ◽  
Hazard Mapping ◽  
Landslide Risk ◽  
Risk Modeling ◽  
Binary Logistic Regression Model ◽  
Linear Features

Landslides constitute a severe environmental problem in Jiangxi, China. This research was aimed at conducting landslide hazard assessment to provide technical support for disaster reduction and prevention action in the province. Fourteen geo-environmental factors, e.g., slope, elevation, road, river, fault, lithology, rainfall, and land cover types, were selected for this study. A test was made in two cases: (1) only based on the main linear features, e.g., main rivers and roads, and (2) with detailed complete linear features including all levels of roads and rivers. After buffering of the linear features, an information value (IV) analysis was applied to quantify the distribution of the observed landslides for each subset of the 14 factors. The results were inputted into the binary logistic regression model (LRM) for landslide risk modeling, taking the known landslide points as a training set (70% of the total 9,525 points). The calculated probability of a landslide was further classified into five grades with an interval of 0.2 for hazard mapping: very high (3.70%), high (4.05%), moderate (18.72%), low (27.17%), and stable zones (46.36%). The accuracy was evaluated by AUC [the area under the receiver operating characteristic (ROC) curve] vs. the validation set (30%, the remaining landslides). The final results show that with increasing the completeness of the linear features, the modeling reliability also significantly increased. We hence concluded that the tested methodology is capable of achieving the landslide hazard prediction at regional scale, and the results may provide technical support for geohazard reduction and prevention in the studied province.

Identification of the affected areas by mass movement through a physically based model of landslide hazard combined with an empirical model of debris flow

2008 ◽  
Vol 45 (2) ◽  
Author(s):  
Roberto A. T. Gomes ◽  
Renato F. Guimarães ◽  
Osmar A. Carvalho ◽  
Nelson F. Fernandes ◽  
Eurípedes A. Vargas ◽  
...  
Keyword(s):  
Debris Flow ◽  
Empirical Model ◽  
Mass Movement ◽  
Landslide Hazard ◽  
Physically Based Model ◽  
Physically Based

scholarly journals Analysis of human exposure to landslides with a GIS multiscale approach

2022 ◽  
Author(s):  
S. Modugno ◽  
S. C. M. Johnson ◽  
P. Borrelli ◽  
E. Alam ◽  
N. Bezak ◽  
...  
Keyword(s):  
Human Exposure ◽  
Local Level ◽  
Explanatory Power ◽  
National Level ◽  
Landslide Hazard ◽  
Landslide Risk ◽  
Multiscale Approach ◽  
Landslide Occurrence ◽  
Multi Scale ◽  
Overview Analysis

AbstractDecision-making plays a key role in reducing landslide risk and preventing natural disasters. Land management, recovery of degraded lands, urban planning, and environmental protection in general are fundamental for mitigating landslide hazard and risk. Here, we present a GIS-based multi-scale approach to highlight where and when a country is affected by a high probability of landslide occurrence. In the first step, a landslide human exposure equation is developed considering the landslide susceptibility triggered by rain as hazard, and the population density as exposed factor. The output, from this overview analysis, is a global GIS layer expressing the number of potentially affected people by month, where the monthly rain is used to weight the landslide hazard. As following step, Logistic Regression (LR) analysis was implemented at a national and local level. The Receiver Operating Characteristic indicator is used to understand the goodness of a LR model. The LR models are defined by a dependent variable, presence–absence of landslide points, versus a set of independent environmental variables. The results demonstrate the relevance of a multi-scale approach, at national level the biophysical variables are able to detect landslide hotspot areas, while at sub-regional level geomorphological aspects, like land cover, topographic wetness, and local climatic condition have greater explanatory power.

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