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Abstract
A region of 1 000 km2 in Enshi, Hubei Province is chosen as the typical study area, and its historical landslide data are comprehensively analyzed in this paper. The strata in the study area are divided into three types including high, middle and low susceptible petrofabrics according to the relationship between local landslide formation and lithology. The scatter diagrams about effective rainfall intensity and critical duration are obtained based on rainfall monitoring data and historical information of landslides in each petrofabric. Thus effective rainfall intensity thresholds are determined and landslide hazard warning model of the study area is suggested. In this study, landslide prediction evaluating system is firstly established based on data of sample area, and then landslide susceptibility distribution map is obtained by using GIS. According to Different Susceptible Petrofabric-Effective Rainfall Intensity Models, the landslide hazard warning is realized by overlaying landslide susceptibility distribution map and rainfall risk grade distribution results. Results show that the hazard warning results fit well with the actual situation. Thus, the warning model is verified to be effective, accurate and comprehensive to provide scientific evidence for preventing and reducing disasters.
... Choosing the appropriate criteria for defining rainfall thresholds is necessary to establish an accurate rainfall warning system for landslides. The intensity (I) and duration (D) of rainfall are the most commonly utilized criteria 25,27,28 . For example, Caine 29 was the first to establish an empirical power law between I and D to define ...
... In addition, cumulative rainfall is also widely used 26,30 , as well as antecedent rainfall conditions and other thresholds, including hydrological thresholds 23,31 . However, current research generally considers the conditions that have initiated landslides in the past as dependent variables in statistical black box models 28,32 , and these results are more applicable to presliding warnings. Apart from landslides which were induced suddenly, there are many landslides with creep deformation that exist in the TGRA, with annual displacements from tens to hundreds of millimeters, but few researchers have focused on the rainfall thresholds for these. ...
... From long-term experiences of prevention and control of landslide hazards in the TGRA, we can determine the emergency measures corresponding different warning levels 28 (Table 4). Among these measures, CBDRR system, professional monitoring and risk management are very common in the TGRA and have been implemented in the field for many years 28,35,37 . ...
Establishing an efficient regional landslide rainfall warning system plays an important role in landslide prevention. To forecast the performance of landslides with creep deformation at a regional scale, a black box model based on statistical analysis was proposed and was applied to Yunyang County in the Three Gorges Reservoir area (TGRA), China. The data samples were selected according to the characteristics of the landslide displacement monitoring data. Then, the rainfall criteria applied to different time periods were determined by correlation analysis between rainfall events and landslides and by numerical simulation on landslide movement under certain rainfall conditions. The cumulative rainfall thresholds that were determined relied on the displacement ratio model, which considered landslide scale characteristics and the statistical relationship between daily rainfall data and monthly displacement data. These thresholds were then applied to a warning system to determine a five-level warning partition of landslides with creep deformation in Yunyang County. Finally, landslide cases and displacement monitoring data were used to validate the accuracy of the model. The validation procedure showed that the warning results of the model fit well with actual conditions and that this model could provide the basis for early warning of landslides with creep deformation.
... In the present scenario landslide has attracted the attention due to the increasing awareness of the social, economic impact of landslides and increasing urbanization on the mountain environment (Aleotti and Chowdhury 1999). The different condition under which landslides can be triggered are intense rainfall, earthquake, variation of water level, snowmelt, etc. (Jiang et al., 2016, Yang et al., 2016, Xie et al., 2015, Wu et al., 2014, Dai and Lee 2002, Keefer 1999. The different influencing factors for the landslides are structural, lithological, geomorphologic, climatic, environmental, hydrological, seismological conditions and the anthropogenic activities of the area and Sikkim faces the similar fate. ...
Landslides in Sikkim results into huge loss of lives and properties with damages to road and other infrastructures. The economy of the state is severely affected by these slope failure problems. Hence the study of slope failures, identification of the locations and causes is of utmost importance. For the mitigation and prevention of the damages, slope study and their stabilization require detailed analysis and understandings. This study basically deals with the recognition of slope failure zones with emphasis on damage and loss to the environment and society. Detailed field work carried out in the Rongli Fatak-Lingtam road section, East Sikkim for identifying the landslide locations, lithologies, structures and the causative factors of landslides. Remote sensing images like Google earth image and topographic maps of Survey of India used to prepare the base map of study area. In total 15 landslides identified in the study area and out of those 13 of them identified to be vulnerable. Kinematic analysis using software Dips 6.0 carried out to identify failure types and the results reveal that all the failures are planar type. Present study is useful in recognizing weak zones and in suggesting mitigation measures for the landslide stabilization.
... The probability method also takes the occurrence of landslides as the research object, but it is often based on probability density function. The probability method mainly focuses on the time probability problem, and carries out probability analysis according to effective rainfall parameters (Wu et al., 2014c;Zhao et al., 2020). ...
... Landslide has attracted the world-wide attention as the awareness is increasing day by day about the socio-economic impact of landslides and increasing of urban area on the mountain environment (Aleotti and Choudhury, 1999). Under several circumstances landslides may be caused due to heavy precipitation, earthquake, water level variations, snow melting, typhoon, etc. (Keefer, 1999;Dai et al., 2002;Wu et al., 2014;Xie et al., 2015;Jiang et al., 2016;Yang et al., 2016). The structural, lithological, geomorphological, climate, environmental, hydrological, and seismological conditions of the region typically affect landslides. ...
Landslide being among the top 5 natural disasters in terms of death and property damages; its susceptibility mapping is important in the landslide-prone zones, especially in the mountainous terrain. The present study deals with the statistical-based Information Value Model for the assessment of landslide susceptibility. The statistically-based approach to calculate the weighted value of the landslide conditioning factor class gives an unbiased rating. For this study, eleven landslide conditioning factors, i.e., slope aspect, slope gradient, slope curvature, drainage density, elevation, lithology, land use and land cover (LULC), normalized difference vegetation index (NDVI), geomorphology, lineament density and soil type covering all the environmental, topographic and geological area were analyzed in the remote sensing (RS) and geographic information system (GIS) environment. The RS and GIS software used for the analysis were ArcGIS and ERDAS Imagine. Each class of these landslide inducing factors was given the rating for the preparation of the landslide susceptibility map for the West Sikkim of Indian Himalaya using the Information Value Model. Further, the accuracy of the model was measured with a receiver operating characteristic (ROC) curve value, which is 0.74 for the present study and is a reliable value. The highest weighted values were obtained for the High Mountain class (1.66) and Built-up class (1.64) of the factors geomorphology and land use and land cover (LULC), respectively. This also infers the high impact of the anthropogenic intervention on the occurrence of slope failures in the mountainous areas. The landslide susceptibility map was prepared based on the ROC curve for the present analysis and was demarcated into three zones based on the severity of the slope failures i.e., high susceptibility zone (6.65 %), moderate susceptibility zone (43.25 %) and low susceptibility zone (50.10 %).
... Landslide has attracted the world-wide attention as the awareness is increasing day by day about the socio-economic impact of landslides and increasing of urban area on the mountain environment (Aleotti and Choudhury, 1999). Under several circumstances landslides may be caused due to heavy precipitation, earthquake, water level variations, snow melting, typhoon, etc. (Keefer, 1999;Dai et al., 2002;Wu et al., 2014;Xie et al., 2015;Jiang et al., 2016;Yang et al., 2016). The structural, lithological, geomorphological, climate, environmental, hydrological, and seismological conditions of the region typically affect landslides. ...
Landslide being among the top 5 natural disasters in terms of death and property damages; its susceptibility mapping is important in the landslide-prone zones, especially in the mountainous terrain. The present study deals with the statistical-based Information Value Model for the assessment of landslide susceptibility. The statistically-based approach to calculate the weighted value of the landslide conditioning factor class gives an unbiased rating. For this study, eleven landslide conditioning factors, i.e., slope aspect, slope gradient, slope curvature, drainage density, elevation, lithology, land use and land cover (LULC), normalized difference vegetation index (NDVI), geomorphology, lineament density and soil type covering all the environmental, topographic and geological area were analyzed in the remote sensing (RS) and geographic information system (GIS) environment. The RS and GIS software used for the analysis were ArcGIS and ERDAS Imagine. Each class of these landslide inducing factors was given the rating for the preparation of the landslide susceptibility map for the West Sikkim of Indian Himalaya using the Information Value Model. Further, the accuracy of the model was measured with a receiver operating characteristic (ROC) curve value, which is 0.74 for the present study and is a reliable value. The highest weighted values were obtained for the High Mountain class (1.66) and Built-up class (1.64) of the factors geomorphology and land use and land cover (LULC), respectively. This also infers the high impact of the anthropogenic intervention on the occurrence of slope failures in the mountainous areas. The landslide susceptibility map was prepared based on the ROC curve for the present analysis and was demarcated into three zones based on the severity of the slope failures i.e., high susceptibility zone (6.65 %), moderate susceptibility zone (43.25 %) and low susceptibility zone (50.10 %).
... For example, a regional model of landslide event occurrence probability under rainfall conditions was obtained in New Zealand based on empirical statistics (Glade et al. 2000). Similarly, the regional rainfall threshold was determined under different risk levels based on the relationship between effective rainfall intensity and critical rainfall duration (Wu et al. 2014;Li et al. 2010). However, this type of model only provides empirical statistics on landslide disasters and rainfall events and ignores the physical mechanisms underlying landslide disasters, and it presents obvious fuzziness and indeterminacy (Martelloni et al. 2012). ...
The traditional Stability INdex MAPping (SINMAP) model does not perform detailed divisions of study areas and neglects differences caused by the asymmetrical spatial distribution of geotechnical parameters; thus, the accuracy of the evaluation results is insufficient. In this study, the evaluation results of the SINMAP model were improved based on a combination with the certainty factor (CF) model, and the proposed method is referred to as the CF-SINMAP model. The Wuling Mountain area in Cili County of Hunan Province (China) was selected to verify the CF-SINMAP model. First, eight geological environmental factors in the region were analyzed by the CF method, including the slope, distance from fault, slope direction, distance from water, rock and soil type, elevation, distance from road and vegetation coverage. The rock and soil type, vegetation coverage and human engineering activities were determined as the key factors underlying landslide hazards. Then, the study area was divided into six regions based on the key factors, and the physical and mechanical parameters of each region were refined by the natural environment, formation lithology and human activities. Finally, the CF-SINMAP model was used to calculate and analyze the landslide hazard assessment results under different rainfall conditions. The results show that the CF-SINMAP model is more sensitive to rainfall compared with the traditional method and the unstable areas are mainly distributed along river valleys, reservoir banks and areas with continual human engineering activities. The area under the receiver operating characteristic (ROC) curve values was 0.75 and 0.61 for the CF-SINMAP and SINMAP models, respectively. Compared with the traditional SINMAP model, the CF-SINMAP model produces more reliable results. The rainfall threshold that induced the landslide disaster in Cili County, Hunan Province, was 90 mm/d. In summary, the CF-SINMAP model provides new ideas for the prediction of regional rainfall-induced landslides.
... The existing landslide early warning is mainly a single monitoring of landslide deformation, mainly through statistical technology [3] , Internet of things technology [4,5,6] , Beidou navigation technology [7,8] and deep displacement Monitoring [9,10] and other technologies. Reference [11] uses intelligent robot technology and relative displacement monitoring technology for surface cracks in the Three Gorges Reservoir area, which can quickly obtain deformation data and improve the speed of early warning and prediction. ...
Landslide is one of the most harmful geological disasters in the world. In order to effectively warn the landslide, Kalman filter is used to smooth the real-time Kinematic (RTK) positioning information of each monitoring point, remove outliers, improve monitoring accuracy, and extract information such as effective displacement. The attitude computation algorithm is used to solve the real-time attitude of the monitoring target, and the attitude prediction is realized which based on the deformation prediction information. Use the extended Kalman filter to realize the fusion of displacement deformation, velocity and acceleration data at multiple sites in the monitoring network, and to achieve the optimal estimation of comprehensive displacement. Use the MGM (1,1) gray model algorithm to realize the deformation displacement prediction. Use the comprehensive information amount to judge the landslide deformation grade. The early-warning algorithm is simple, easy to implement and practical, and can meet the actual requirements of landslide deformation early-warning.
... Landslides could be triggered under several conditions, e.g. intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon etc. [11][12][13][14][15][16]. Landslides are generally influenced by the action of structural, lithological, geomorphological, climatic, environmental, hydrological, seismological conditions etc. of the affected area. ...
Abstract Occurrence of landslides is very common and frequent phenomenon in hilly terrain of Indian Himalayan region leading to severe environmental and socio-economic issues. The current research used the method of weighted parameter, Remote Sensing (RS) and Geographic Information System (GIS) for landslide susceptibility mapping in the study area, East Sikkim district of Sikkim Himalaya. The different thematic layers were produced from high-resolution terrain corrected ALOS PALSAR DEM of 12.5 meter spatial resolution, Sentinel-2A data of 10 meter spatial resolution multi-spectral satellite information, LANDSAT 8 multi-spectral satellite information and multiple other landslide-related sources such as rainfall distribution, slope and structural/linear features (faults, thrusts, roads). These thematic map layers were integrated in a GIS platform (ArcGIS10.7) to delineate vulnerable landslide prone zones. The weighted assigned values were used for assigning weightage ranging from 0 to 10 for various causative factors responsible for landslide occurrences using standard weighted overly techniques. Landslide susceptibility map of the entire research area is split into three categories i.e. low susceptibility, medium susceptibility and high susceptibility. The final map of the landslide susceptibility was further validated with GPS location information gathered from the field survey of active landslide locations. This research would be helpful in the study region for adequate planning of future development of infrastructure, landslide hazard prevention, and geoenvironmental development.
... Landslides could be triggered under several conditions, e.g. intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon etc. [11][12][13][14][15][16]. Landslides are generally influenced by the action of structural, lithological, geomorphological, climatic, environmental, hydrological, seismological conditions etc. of the affected area. ...
Abstract Occurrence of landslides is very common and frequent phenomenon in hilly terrain of Indian Himalayan region leading to severe environmental and socio-economic issues. The current research used the method of weighted parameter, Remote Sensing (RS) and Geographic Information System (GIS) for landslide susceptibility mapping in the study area, East Sikkim district of Sikkim Himalaya. The different thematic layers were produced from high-resolution terrain corrected ALOS PALSAR DEM of 12.5 meter spatial resolution, Sentinel-2A data of 10 meter spatial resolution multi-spectral satellite information, LANDSAT 8 multi-spectral satellite information and multiple other landslide-related sources such as rainfall distribution, slope and structural/linear features (faults, thrusts, roads). These thematic map layers were integrated in a GIS platform (ArcGIS10.7) to delineate vulnerable landslide prone zones. The weighted assigned values were used for assigning weightage ranging from 0 to 10 for various causative factors responsible for landslide occurrences using standard weighted overly techniques. Landslide susceptibility map of the entire research area is split into three categories i.e. low susceptibility, medium susceptibility and high susceptibility. The final map of the landslide susceptibility was further validated with GPS location information gathered from the field survey of active landslide locations. This research would be helpful in the study region for adequate planning of future development of infrastructure, landslide hazard prevention, and geoenvironmental development. Keywords: landslides, landslide susceptibility mapping, Sikkim Himalaya, DEM, GIS
... Landslides could be triggered under several conditions, e.g. intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon etc. [11][12][13][14][15][16]. Landslides are generally influenced by the action of structural, lithological, geomorphological, climatic, environmental, hydrological, seismological conditions etc. of the affected area. ...
Occurrence of landslides is very common and frequent phenomenon in hilly terrain of Indian Himalayan region leading to severe environmental and socio-economic issues. The current research used the method of weighted parameter, Remote Sensing (RS) and Geographic Information System (GIS) for landslide susceptibility mapping in the study area, East Sikkim district of Sikkim Himalaya. The different thematic layers were produced from high-resolution terrain corrected ALOS PALSAR DEM of 12.5 meter spatial resolution, Sentinel-2A data of 10 meter spatial resolution multi-spectral satellite information, LANDSAT 8 multi-spectral satellite information and multiple other landslide-related sources such as rainfall distribution, slope and structural/linear features (faults, thrusts, roads). These thematic map layers were integrated in a GIS platform (ArcGIS10.7) to delineate vulnerable landslide prone zones. The weighted assigned values were used for assigning weightage ranging from 0 to 10 for various causative factors responsible for landslide occurrences using standard weighted overly techniques. Landslide susceptibility map of the entire research area is split into three categories i.e. low susceptibility, medium susceptibility and high susceptibility. The final map of the landslide susceptibility was further validated with GPS location information gathered from the field survey of active landslide locations. This research would be helpful in the study region for adequate planning of future development of infrastructure, landslide hazard prevention, and geo-environmental development.
... Landslides could be triggered under several conditions, e.g. intense rainfall, earthquake shaking, variation of water level, snowmelt, typhoon etc. [11][12][13][14][15][16]. Landslides are generally influenced by the action of structural, lithological, geomorphological, climatic, environmental, hydrological, seismological conditions etc. of the affected area. ...
... With the help of the surface tool on ArcGIS 10.2 platform, the data of elevation, slope gradient, slope aspect and curvature of the study area were obtained from 5 m DEM. The slope position was extracted based on slope gradients obtained from 5 m DEM by using the topographic position index (TPI) tools (Weiss, 2001). Using hydrology tool of ArcGIS, the data on major drainages for the study area were achieved. ...
On July 22, 2013, an earthquake (Ms 6.6) occurred in Minxian, Gansu Province of China,
causing a large number of landslides. Based on high resolution remote sensing images before and after this
event, we made the visual interpretation to these coseismic landslides, and prepared a detailed inventory.
The inventory registers totally 6 478 landslides in the study area. Of them, 3 322 landslides are larger than
100 m 2 . Based on 5 m resolution DEM, these landslides were used to perform spatial analyses using
landslide number density (LND) and landslide area percentage (LAP). The results show that the highest
LND and LAP values are in the elevation range of 2 300–2 500 m and steeper slopes. Slopes facing E, SE, S
and SW directions, slopes with larger absolute curvature values, ridges, scopes of gravel beds of Late
Pleistocene (Q p ) and the VIII-degree seismic intensity are more prone to sliding with high LND and LAP
values. The largest LND and LAP values are in the scopes of 0.08 and 0.24 g, respectively. According to
landslide distribution, we infer that F2-2 branch of Lintan-Dangchang fault is the seismogenic fault. With
the increasing distances to this branch fault and drainages, LND and LAP values tend to decrease.
... With the help of the surface tool on ArcGIS 10.2 platform, the data of elevation, slope gradient, slope aspect and curvature of the study area were obtained from 5 m DEM. The slope position was extracted based on slope gradients obtained from 5 m DEM by using the topographic position index (TPI) tools (Weiss, 2001). Using hydrology tool of ArcGIS, the data on major drainages for the study area were achieved. ...
On July 22, 2013, an earthquake (Ms 6.6) occurred in Minxian, Gansu Province of China, causing a large number of landslides. Based on high resolution remote sensing images before and after this event, we made the visual interpretation to these coseismic landslides, and prepared a detailed inventory. The inventory registers totally 6 478 landslides in the study area. Of them, 3 322 landslides are larger than 100 m2. Based on 5 m resolution DEM, these landslides were used to perform spatial analyses using landslide number density (LND) and landslide area percentage (LAP). The results show that the highest LND and LAP values are in the elevation range of 2 300–2 500 m and steeper slopes. Slopes facing E, SE, S and SW directions, slopes with larger absolute curvature values, ridges, scopes of gravel beds of Late Pleistocene (Qp) and the VIII-degree seismic intensity are more prone to sliding with high LND and LAP values. The largest LND and LAP values are in the scopes of 0.08 and 0.24 g, respectively. According to landslide distribution, we infer that F2-2 branch of Lintan-Dangchang fault is the seismogenic fault. With the increasing distances to this branch fault and drainages, LND and LAP values tend to decrease.
Landslide hazard assessment is essential for determining the probability of landslide occurrence in a specific spatial and temporal range. The hazard assessment of potential landslides could support landslide disaster early warning and disaster prevention decisions, which have important guiding significance for urban construction and sustainable development. Due to the lack of consideration of the synergistic effect of multiple factors and geographic scene heterogeneity, the accuracy of existing landslide hazard assessment methods still needs to be improved, and the interpretability and applicability of existing models still need to be improved. In this paper, we propose a landslide hazard assessment method considering the synergistic effect of multiple factors, including natural factors and human activities, and the heterogeneity of geographic scenes. On this basis, we carry out experimental verification on rainfall–induced landslides in Dehong Prefecture, Yunnan Province, China. Firstly, rainfall–induced landslide hazards’ characteristics and impact factors are analyzed and classified. The whole study area is divided into some homogeneous sub–regions using regional dynamic constraint clustering based on the similarity of underlying environmental variables. Then, considering the spatial autocorrelation between various landslide conditioning and trigger factors, a local weighted random forest model is developed to evaluate the rainfall–induced landslide hazards comprehensively. Experimental results show that the proposed method has higher accuracy and interpretability than the existing representative methods and can provide useful references for preventing landslide hazards.
This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction (LSP), namely the spatial resolution, proportion of model training and testing datasets and selection of machine learning models. Taking Yanchang County of China as example, the landslide inventory and 12 important conditioning factors were acquired. The frequency ratios of each conditioning factor were calculated under five spatial resolutions (15, 30, 60, 90 and 120 m). Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets (9:1, 8:2, 7:3, 6:4 and 5:5), and four typical machine learning models were applied for LSP modelling. The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty. With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5, the modelling accuracy gradually decreased, while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased. The sensitivities of the three uncertainty issues to LSP modeling were, in order, the spatial resolution, the choice of machine learning model and the proportions of training/testing datasets.
The establishment of the multi-dimensional meteorological early warning criterion of landslide and the division of the "grid" early warning unit can provide a scientific basis for the landslide early warning. 205 rainfall-induced landslides in Panan County, Zhejiang Province were proposed in this paper. Firstly, based on the average effective rainfall intensity-diachronic (I-D) threshold model, the critical threshold curves were divided by ordinary least squares regression (OLSQ) and quantile regression (QR). Secondly, the I-D-R threshold model was established by the I-D threshold model optimized by considering the daily rainfall (R), and different parameter estimation methods were used to compare the
accuracy of different threshold models. The optimal threshold model was considered as the meteorological early warning criterion for landslide disasters in Pan'an County. Finally, considering the difference of rainfall distribution, the township level grid early warning unit was established by the terrain zoning and V oronoi diagram (VD) of Pan'an. The results showed that: 1) the I-D-R threshold model had better early warning accuracy than the I-D model. The I-D-R threshold model based on QR had a better warning ability, and the accuracy of the threshold degree of warning and above is increased to 50%, and the accuracy of the threshold level of special attention and above is increased to 88.8%; 2). The rainfall conditions with I-D-R based on QR rainfall threshold was proposed as the early warning criteria (red, orange, yellow and blue) of Pan'an County 51 early warning units, and the following emergency response measures were put forward. These results provide a new threshold model, which can provide reference for regional meteorological early warning in Pan'an County.
In order to realize the prediction on the slope stability,the slope in Dingjiafen in Chuxiong, Yunnan, China was selected as the worksite to be studied,the critical soil thickness (D cr ) when the landslide was started under saturated condition of slope and the maximum soil thickness（D max ）when the landslide was started under unsaturated condition of slope were inferred by using the slope stability physical model. Based on digital elevation model (DEM), the critical soil thickness and maximum soil thickness of each slope unit at the start of landslide were calculated,the criterion of slope instability was determined, and the influence of rainfall infiltration process, soil thickness, the physical and mechanical properties of soil, slope surface and the bedrock surface topography on slope stability was analyzed.The analysis results were verified by means of modeling and numerical simulation calculation of FLAC3D. The research results shows that the critical soil thickness and the maximum soil thickness of the slope unite with different topographies at the start of landslide are not the same. Besides, the slope units with the critical soil thickness between 1m~3m were located at the right and left side of the Chu-Meng highway, which conformed with the actual position of sliding. Simulating the thickness of soil layer when slope units were at the critical state of landslide by using this method can predict the stability of slopes.
Rainfall-induced landslide hazard warning, which refers to the prediction of the spatial-temporal probability of landslide occurrence in a certain area under the conditions of continuous rainfall processes, can be established based on landslide susceptibility mapping and critical rainfall threshold calculations. However, it is difficult to determine appropriate machine learning models for mapping landslide susceptibility. Additionally, it is significant to consider the influences of early effective rainfall on landslide instability in the critical rainfall threshold methods. Furthermore, the uncertainties of the critical rainfall threshold values generated by different calculation methods have not been well explored. To overcome these three drawbacks, first, frequency ratio analysis-based logistic regression (LR), support vector machine (SVM) and random forest (RF) models are adopted to predict landslide susceptibility for machine learning model comparison. Second, three different types of critical rainfall threshold methods, namely, cumulative effective rainfall-duration (EE-D), effective rainfall intensity-duration (EI-D) and cumulative effective rainfall-effective rainfall intensity (EE-EI) models, are proposed to calculate the temporal probabilities of landslide occurrence under rainfall conditions based on the concept of effective rainfall. The accuracies and uncertainties of these three critical rainfall threshold methods are discussed. Finally, the landslide susceptibility maps and the critical rainfall threshold values are coupled to predict the rainfall-induced landslide hazards. Xunwu County in China is selected as the study area, and several rainfall-induced landslides are used as the test samples of the proposed landslide hazard warning model. The results show that the RF model has remarkably higher susceptibility prediction accuracy than the SVM and LR models, and the prediction performance of the temporal probabilities of landslide occurrence using the EI-D values are higher than those of EE-D and EE-EI values. Furthermore, rainfall-induced landslide hazard warning is effectively implemented based on the coupling of the susceptibility map and EI-D model.
Harmful landslides like other hilly regions of India have
affected Sikkim Himalayas; these disasters are triggered
naturally as well as due to intervention of anthropogenic
activities. The rapid urbanization in the hilly terrains and
consequently exploited for the benefits of the dwellers of the
state. At present the increasing in number of landslides are due
to the extension of anthropogenic activities in the different
areas. Loss of lives and property along with economic losses in
the state is a big challenge. Hampering of road traffics in
Sikkim is very much affected due to landslides and the tourism
which is the major source of revenue in the state is heavily
affected. Yuksom is a well known tourist destination in the
state. The present study carried out on Yuksom- Tashiding road
section of West Sikkim district in the state of Sikkim. The roads
are being affected by slope failures especially during the
monsoon season. The geology plays an important role in
triggering of landslide along with other factors. The area
comprises of rocks belongs to Daling Group, which consists of
phyllites and quartzites rocks. As per the nature of phyllite,
when wet it loses 25% shear strength as compared to dry rocks,
hence this becomes highly vulnerable for landslides during
monsoon period. The geological mapping of the landslides
using Survey of India topographic maps, satellite imagery,
Google earth, landslide inventory from published literature and
field survey. By integrating all the data in the ArcGis 10.6 and
Erdas Imagine 2016 proper delineation of the slope failures
done. A total of 15 landslides studied to understand their types
and causative factors.
Harmful landslides like other hilly regions of India have affected Sikkim Himalayas; these disasters are triggered naturally as well as due to intervention of anthropogenic activities. The rapid urbanization in the hilly terrains and consequently exploited for the benefits of the dwellers of the state. At present the increasing in number of landslides are due to the extension of anthropogenic activities in the different areas. Loss of lives and property along with economic losses in the state is a big challenge. Hampering of road traffics in Sikkim is very much affected due to landslides and the tourism which is the major source of revenue in the state is heavily affected. Yuksom is a well known tourist destination in the state. The present study carried out on Yuksom-Tashiding road section of West Sikkim district in the state of Sikkim. The roads are being affected by slope failures especially during the monsoon season. The geology plays an important role in triggering of landslide along with other factors. The area comprises of rocks belongs to Daling Group, which consists of phyllites and quartzites rocks. As per the nature of phyllite, when wet it loses 25% shear strength as compared to dry rocks, hence this becomes highly vulnerable for landslides during monsoon period. The geological mapping of the landslides using Survey of India topographic maps, satellite imagery, Google earth, landslide inventory from published literature and field survey. By integrating all the data in the ArcGis 10.6 and Erdas Imagine 2016 proper delineation of the slope failures done. A total of 15 landslides studied to understand their types and causative factors.
Abstract: Statistical and machine learning models, such as Support Vector Machine (SVM), have been widely used to assess the landslide susceptibility. However, the modeling processes of statistical and machine learning model are generally complex. For example, it is difficult to select reasonable non-landslide grid cells when the machine learning models are trained and tested, and many model parameters need to be determined. In order to improve the efficiency and accuracy of the model used for landslide susceptibility assessment, the Grey Relational Degree (GRD) model is proposed. The GRD model can efficiently calculate the quantitative relational degrees between the comparative samples and the reference sample, and it has the advantages of simple modeling process and accurate assessment results. However, the GRD model has received little attention from the researchers. In this study, the GRD model is used to assess the landslide susceptibility in the Nantian and Yamei maps (Nantian area) in the Feiyunjiang river basin, Zhejiang province of China, and the assessment results of the GRD model is compared with the SVM model. The results show that the GRD model has higher prediction rate than the SVM model in the high and very high susceptibility areas, and has slightly lower prediction rate than the SVM in the moderate susceptibility area. On the whole, the GRD model has slightly higher prediction rate than the SVM for landslide susceptibility assessment in Nantian area. Meanwhile, The results also show that the model process of GRD is simple, it has higher efficiency than the SVM. The GRD model provides a novel idea for landslide susceptibility assessment.
This paper describes the potential applicability of a hydrological–geotechnical modeling system using satellite-based rainfall
estimates for a shallow landslide prediction system. The physically based distributed model has been developed by integrating
a grid-based distributed kinematic wave rainfall-runoff model with an infinite slope stability approach. The model was forced
by the satellite-based near real-time half-hourly CMORPH global rainfall product prepared by NOAA-CPC. The method combines
the following two model outputs necessary for identifying where and when shallow landslides may potentially occur in the catchment:
(1) the time-invariant spatial distribution of areas susceptible to slope instability map, for which the river catchment is
divided into stability classes according to the critical relative soil saturation; this output is designed to portray the
effect of quasi-static land surface variables and soil strength properties on slope instability and (2) a produced map linked
with spatiotemporally varying hydrologic properties to provide a time-varying estimate of susceptibility to slope movement
in response to rainfall. The proposed hydrological model predicts the dynamic of soil saturation in each grid element. The
stored water in each grid element is then used for updating the relative soil saturation and analyzing the slope stability.
A grid of slope is defined to be unstable when the relative soil saturation becomes higher than the critical level and is
the basis for issuing a shallow landslide warning. The method was applied to past landslides in the upper Citarum River catchment
(2,310 km2), Indonesia; the resulting time-invariant landslide susceptibility map shows good agreement with the spatial patterns of
documented historical landslides (1985–2008). Application of the model to two recent shallow landslides shows that the model
can successfully predict the effect of rainfall movement and intensity on the spatiotemporal dynamic of hydrological variables
that trigger shallow landslides. Several hours before the landslides, the model predicted unstable conditions in some grids
over and near the grids at which the actual shallow landslides occurred. Overall, the results demonstrate the potential applicability
of the modeling system for shallow landslide disaster predictions and warnings.
KeywordsCMORPH satellite-based rainfall-Distributed model-Hydrology-Shallow landslide-Slope stability-Citarum River catchment
A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70?90%.
Based on WEBGIS platform, the paper presents a real-time warning system for regional geological hazards in Zhejiang Province which is formulated with precipitation and its forecasting data and geological hazard investigation data. The system concludes four subsystems: subsystem of geological hazard management; subsystem of spatial prediction and time warning; subsystem of application in illustrative regions and subsystem of hazard reduction and prevention. The main purposes are (1) to establish systematic digital geological hazard management of individual and regional geological hazard information including data and maps according to the subsystem of geological hazard system, which can serves for decision-making in administrative ways; (2) to establish a real-time regional geological hazard warning system in Internet which is strongly connected with weather information center. The warning information of Zhejiang Province is released timely through Web net in raining seasons. The key part of this system is geological hazard prediction and warning modes, which is realized with the combination of regional geological hazard zonation and dynamic weather information. This system is run during the rain season from May to July of 2004. It is evaluated by in-site geological hazard inventory that practical geological hazards are mostly occurred within the warned high risk areas, which gives good confidences of the models and system operations.
Debris flows generated during rainstorms present a greater risk of death and injury to southern California residents than all other kinds of slope failure combined. During the years 1962-1971, twenty-three people in the greater Los Angeles area died from being buried or struck by debris flows, all of which probably originated as soil slips. Soil slips and debris flows are recurring major natural geomorphological processes in the region.
Soil slips are reconstituted into debris flows when the initial movement (sliding failure) of slabs of soil and wedges of ravine fill causes remoulding of the saturated moving mass into viscous, debris-laden mud, which then flows down available drainage courses. This change of state results in a marked reduction in resistance to shear, permitting masses to accelerate down the same slopes on which, only moments earlier, the slabs of soil mantle had barely overcome the resistance to sliding. Many flows reach avalanche speeds and do not begin to deposit significant amounts of detritus until they reach lower gradients far from their sources. The deposits form steep ‘alluvial’ fans at the mouths of short, steep drainage basins tributary to broad valleys, and form debris trains in and along narrow trunk canyons below the mouths of short, steep tributaries.
The exceptional storm period of January 18th to 26th, 1969, was accompanied by thousands of soil slips and provided an unusual opportunity to determine the times of occurrence of numerous debris flows, establish their origin from soil slips, and compare the times of those events with rainfall records from an extensive network of continuously recording rain gauges and a sequential set of radar weather maps. An empirical association between soil slips and rainfall suggests that a 10 inch antecedent rainfall is required to bring most of the colluvial soil of the area to field capacity, and 0.25 inch-per-hour is the minimum rate at which surface infiltration exceeds subsoil drainage for most of the colluvial soils of the area so that pore pressures are raised in a zone above the less permeable parent materials. Soil moisture of this level also appears to be sufficient so that most of the colluvial soils of the area will become at least partly liquid when disturbed.