Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslid...Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.展开更多
An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed.This study shows that such a simulation may be operated in real-time to highlight tho...An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed.This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration(I-D) thresholds.First,the study on landslide susceptibility in China is introduced.The entire territory has been classified into five categories,among which high-susceptibility regions(Zone 4-'High' and 5-'Very high') account for 4.15%of the total extension of China.Second,rainfall is considered as an external triggering factor that may induce landslide initiation.Real-time satellite-based TMPA3B42 products may provide real rainfall spatial and temporal patterns,which may be used to derive rainfall duration time and intensity.By using a historical record of 60 significant past landslides,the rainfall I-D equation has been calibrated.The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours.The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds,where the susceptibility category is 'high' or 'very high'.This study shows a useful tool to be part of a systematic landslide simulation methodology,potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.展开更多
A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil c...A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.展开更多
文摘Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.
基金supported by the National Natural Science Foundation of China(Grant No.41501458)China Postdoctoral Science Foundation Funded Project(Grant No.2016M592860)+4 种基金National Basic Research Program of China(Grant No.2013CB733204)Key Laboratory of Mining Spatial Information Technology of NASMG(Grant Nos. KLM201309)Science Program of Shanghai Normal University(Grant No. SK201525)the Shanghai Gaofeng & Gaoyuan Project for University Academic Program Development(Grant Nos.2013LASW-A09 & SKHL1310)the Center of Spatial Information Science and Sustainable Development Applications,Tongji University,Shanghai,China
文摘An empirical simulation method to simulate the possible position of shallow rainfall-induced landslides in China has been developed.This study shows that such a simulation may be operated in real-time to highlight those areas that are highly prone to rainfall-induced landslides on the basis of the landslide susceptibility index and the rainfall intensity-duration(I-D) thresholds.First,the study on landslide susceptibility in China is introduced.The entire territory has been classified into five categories,among which high-susceptibility regions(Zone 4-'High' and 5-'Very high') account for 4.15%of the total extension of China.Second,rainfall is considered as an external triggering factor that may induce landslide initiation.Real-time satellite-based TMPA3B42 products may provide real rainfall spatial and temporal patterns,which may be used to derive rainfall duration time and intensity.By using a historical record of 60 significant past landslides,the rainfall I-D equation has been calibrated.The rainfall duration time that may trigger a landslide has resulted between 3 hours and 45 hours.The combination of these two aspects can be exploited to simulate the spatiotemporal distribution of rainfall-induced landslide hazards when rainfall events exceed the rainfall I-D thresholds,where the susceptibility category is 'high' or 'very high'.This study shows a useful tool to be part of a systematic landslide simulation methodology,potentially providing useful information for a theoretical basis and practical guide for landslide prediction and mitigation throughout China.
文摘A correct assessment of the landslide susceptibility component is extremely useful for the diminution of associated potential risks to local economic development, particularly in regard to land use planning and soil conservation. The purpose of the present study was to compare the usefulness of two methods, i.e., binary logistic regression(BLR) and analytical hierarchy process(AHP), for the assessment of landslide susceptibility over a 130-km^2 area in the Moldavian Plateau(eastern Romania) region, where landslides affect large areas and render them unsuitable for agriculture. A large scale inventory mapping of all types of landslides(covering 13.7% of the total area) was performed using orthophoto images, topographical maps, and field surveys. A geographic information system database was created, comprising the nine potential factors considered as most relevant for the landsliding process. Five factors(altitude, slope angle, slope aspect, surface lithology, and land use) were further selected for analysis through the application of a tolerance test and the stepwise filtering procedure of BLR. For each predictor, a corresponding raster layer was built and a dense grid of equally spaced points was generated, with an approximately equal number of points inside and outside the landslide area, in order to extract the values of the predictors from raster layers. Approximately half of the total number of points was used for model computation, while the other half was used for validation. Analytical hierarchy process was employed to derive factor weights, with several pair-wise comparison matrices being tested for this purpose. The class weights, on a scale of 0 to 1, were taken as normalized landslide densities. A comparison of results achieved through these two approaches showed that BLR was better suited for mapping landslide susceptibility, with 82.8% of the landslide area falling into the high and very high susceptibility classes. The susceptibility class separation using standard deviation was superior to either the equal interval or the natural break method. Results from the study area suggest that the statistical model achieved by BLR could be successfully extrapolated to the entire area of the Moldavian Plateau.
