Landslide hazard zonation using analytical hierarchy process along National Highway-3 in mid Himalayas of Himachal Pradesh,India

Landslide zonation studies emphasize on preparation of landslide hazard zonation maps considering major instability factors contributing to occurrence of landslides. This paper deals with geographic information system-based landslide hazard zonation in mid Himalayas of Himachal Pradesh from Mandi to Kullu by considering nine relevant instability factors to develop the hazard zonation map. Analytical hierarchy process was applied to assign relative weightages over all ranges of instability factors of the slopes in study area. To generate landslide hazard zonation map, layers in geographic information system were created corresponding to each instability factor. An inventory of existing major landslides in the study area was prepared and combined with the landslide hazard zonation map for validation purpose. The validation of the model was made using area under curve technique and reveals good agreement between the produced hazard map and previous landslide inventory with prediction accuracy of 79.08%. The landslide hazard zonation map was classified by natural break classifier into very low hazard, low hazard, moderate hazard, high hazard and very high landslide hazard classes in geographic information system depending upon the frequency of occurrence of landslides in each class. The resultant hazard zonation map shows that 14.30% of the area lies in very high hazard zone followed by 15.97% in high hazard zone. The proposed model provides the best-fit classification using hierarchical approach for the causative factors of landslides having complex structure. The developed hazard zonation map is useful for landslide preparedness, land-use planning, and social-economic and sustainable development of the region.     

Landslide hazard mapping along national highway-154A in Himachal Pradesh,India using information value and frequency ratio

For the socio-economic development of a country, the highway network plays a pivotal role. It has therefore become an imperative to have landslide hazard assessment along these roads to provide safety. The current study presents landslide hazard zonation maps, based on the information value method and frequency ratio method using GIS on 1:50,000 scale by generating the information about the landslide influencing factors. The study was carried out in the year 2017 on a part of Ravi river catchment along one of the landslide-prone Chamba to Bharmour road corridor of NH-154A in Himachal Pradesh, India. A number of landslide triggering geo-environmental factors like “slope, aspect, relative relief, soil, curvature, Land Use and Land Cover (LULC), lithology, drainage density, and lineament density” were selected for landslide hazard mapping based on landslide inventory. The landslide inventory has been developed using satellite imagery, Google earth and by doing exhaustive field surveys. A digital elevation model was used to generate slope gradient, slope aspect, curvature, and relative relief map of the study area. The other information, i.e., soil maps, geological maps, and toposheets, have been collected from various departments. The landslide hazard zonation map was categorized namely “very high hazard, high hazard, medium hazard, low hazard, and very low hazard.” The results from these two methods have been validated using area under curve (AUC) method. It has been found that hazard zonation map prepared using frequency ratio model had a prediction rate of 75.37% while map prepared using information value method had prediction rate of 78.87%. Hence, on the basis of prediction rate, the landslide hazard zonation map, obtained using information value method, was experienced to be more suitable for the study area.     

浙江省永嘉县区域滑坡灾害人口易损性评价和伤亡风险预测

区域滑坡灾害人口易损性及人口伤亡风险预测研究是区域滑坡灾害预警预报工作的一个重要环节,该研究对提高预警预报工作的针对性和有效性具有关键作用.在对浙江省永嘉县有关资料进行分析的基础上,从研究区人口年龄结构、居民对滑坡灾害风险的防范意识、政府对滑坡灾害的重视程度及滑坡灾害预警预报体系的完善程度4个方面评价了研究区人口易损性,并给出了计算人口易损性的公式,据此得到了永嘉县人口易损性分布图.根据永嘉县的实际情况,提出了耕地人口密度的概念.综合人口易损性分布图、人口密度分布图和滑坡灾害易发性预测图得到了研究区受威胁人口伤亡风险预测图,为当地政府职能部门实施滑坡灾害风险的控制和管理提供决策依据.     

基于要素分析和二元统计模型的区域滑坡危险等级制图——以国道212线陇南段为例

国道212线陇南段是我国地质灾害最发育的地区之一,绘制该区的滑坡危险等级地图对灾害管理和发展规划是极其必要的。基于滑坡的野外调查、机理研究和室内试验等工作,分析了滑坡与各种要素的相关性,选择控制滑坡的9个重要要素作为评价要素,利用GIS和二元统计的信息值模型和滑坡先验风险要素模型绘制了研究区的滑坡危险等级地图。最后,选用区内11个具有明显滑动位移的活动滑坡与滑坡危险等级地图比较,检验其可靠度。结果表明,活动的滑坡绝大部分都位于危险等级很高和高的范围内,说明两种模型的评价结果与研究区实际情况相吻合,同时也反映出信息值模型与实际情况更加相符。     

Inclusion of earthquake strong ground motion in a geographic information system-based landslide susceptibility zonation in Garhwal Himalayas

Garhwal Himalayas are seismically very active and simultaneously suffering from landslide hazards. Landslides are one of the most frequent natural hazards in Himalayas causing damages worth more than one billion US$ and around 200 deaths every year. Thus, it is of paramount importance to identify the landslide causative factors to study them carefully and rank them as per their influence on the occurrence of landslides. The difference image of GIS-derived landslide susceptibility zonation maps prepared for pre- and post-Chamoli earthquake shows the effect of seismic shaking on the occurrence of landslides in the Garhwal Himalaya. An attempt has been made to incorporate seismic shaking parameters in terms of peak ground acceleration with other static landslide causative factors to produce landslide susceptibility zonation map in geographic information system environment. In this paper, probabilistic seismic hazard analysis has been carried out to calculate peak ground acceleration values at different time periods for estimating seismic shaking conditions in the study area. Further, these values are used as one of the causative factors of landslides in the study area and it is observed that it refines the preparation of landslide susceptibility zonation map in seismically active areas like Garhwal Himalayas.     

A comparison of the predicted vulnerability zones with the data based on hazard zones of landslide in the Kurseong hill subdivision,Darjeeling district,West Bengal,India

Kurseong hill subdivision, being one of the three (Kurseong, Sadar and Kalingpong) subdivisions of the hilly portions of the Darjeeling district, West Bengal, India, is affected by severe landslide incidence in the rainy season every year. These landslides and related phenomena frequently create social and economic instability disrupting communication system, claiming property and even sometimes life. Curbing landslide threat, therefore, becomes very much essential over this area. Individual landslide treatments are seen to be taken up by the construction engineers and geo-technical experts almost every year from government level. But reoccurrence of landslides on the same spots or surrounding places clearly reveals that construction works and filling procedures (usually taken up) are not the adequate measures to heal up the problem unless the area is treated as zones of landslides than individual spots of landslide occurrences. Therefore, the assessment of spatial probability of landslide occurrence in various magnitudes in the form of landslide vulnerability zones becomes essential. This spatial probability should also be compared with temporal probability based on the data of landslide incidence of the area for justification of match or mismatch between the inference drawn from the diagnostic criteria based assessment of the possibility level of landslide occurrence and the reality of the landslide scenario in the light of historical perspective of the area. This comparison will finally help to achieve the predicted vulnerability zones of landslide with desirable accuracy to put forward for planning decision. Moreover, such predicted vulnerability zonation can be taken as a standard estimate to use in planning purpose for the areas where historical data of landslide incidences are inadequate or unavailable. With this view in mind, the present paper takes an attempt to verify and compare landslide vulnerability zones derived from Spatial Terrain Parameter Evaluation (STPE) and Anthropogenic Criteria Identification (ACI) methods with the landslide hazard zones prepared from historical data, i.e. landslide inventory of certain length of time. Careful observation reveals that different degrees of landslide vulnerability zones significantly correspond with the similar magnitudes of the landslide hazard zones determined by past occurrence data of landslides over this hill subdivision and therefore validate the predictability procedure of landslide vulnerability zonation. The accuracy performance of the landslide vulnerability zonation model has further been verified by the occurrence dataset of landslide events through receiver operating characteristic curve analysis where area under curve evaluation showed 81.77 % correctness.     

Integrated use of GIS and remote sensing for monitoring landslides in transportation pavements: the case study of Paphos area in Cyprus

This study considers the impact of landslides on transportation pavements in rural road network of Cyprus using remote sensing and geographical information system (GIS) techniques. Landslides are considered to be one of the most extreme natural hazards worldwide, causing both human losses and severe damages to the transportation network. Risk assessment for monitoring a road network is based on the combination of the probability of landslides occurrence and the extent and severity of the resultant consequences should the disasters (landslides) occur. Factors that can trigger landslide episodes include proximity to active faults, geological formations, fracture zones, degree and high curvature of slopes, water conditions, etc. In this study, the reliability and vulnerability of a rural network are examined. Initially, landslide locations were identified from the interpretation of satellite images. Different geomorphological factors such as aspect, slope, distance from the watershed, lithology, distance from lineaments, topographic curvature, land use and vegetation regime derived from satellite images were selected and incorporated in GIS environment in order to develop a decision support and continuous landslide monitoring system of the area. These parameters were then used in the final landslide hazard assessment model based on the analytic hierarchy process method. The results indicated good correlation between classified high-hazard areas and field-confirmed slope failures. The CA Markov model was also used to predict the landslide hazard zonation map for 2020 and the possible future hazards for transportation pavements. The proposed methodology can be used for areas with similar physiographic conditions all over the Eastern Mediterranean region.     

Mapping of landslide hazard zonation using GIS at Golestan watershed, northeast of Iran

Landslide hazard zonation is essential for planning future developmental activities. At the present study, after the preparation of a landslide inventory of the study area, nine factors as well as sub-data layers of factor class weights were tested for an integrated analysis of landslide hazard in the region. The produced factor maps were weighted with the analytic hierarchy process method and then classified into four classes—negligible, low, moderate, and high. The final produced map for landslide hazard zonation in Golestan watershed revealed that: (1) about 53.85 % of the basin is prone to moderate and high threats of landslides. (2) Landslide events at the Golestan watershed were strongly correlated to the slope angle of the basin. It was observed that the active landslide zones, including moderate to high landslide hazard classes, have a high correlation to slope classes over 30° (R ²?=?0.769). (3) The regions most susceptible to landslide hazard are those located south and southwest of the watershed, which included rock topples, falls, and debris landslides.     

Relative effect-based landslide hazard zonation mapping in parts of Nilgiris, Tamil Nadu, South India

The Nilgiri massif, South India, is chronically prone to landslides due to deforestation and the resultant direct entry of rainwater and the final increases of pore pressure leading to landslides in the region. In order to understand such landslide causes, the relative effect method, a new technique, has been adopted for the study area. Among various methods, this is a statistical method developed within the framework of the Geographic Information System to map landslide hazard zones in a mountainous environment. To determine the relative effect (RE) of the factors influencing landslides, data layers of geology, land use/land cover, geomorphology, slope, lineament density, drainage density, and soil were analyzed by calculating the ratio of the unit portion in coverage and landslide, this function that is logarithmic. To quantify the magnitude of factors influencing each grid unit, REs were summed and classified into zones of low-, moderate-, and high-landslide hazard zones. It is also appropriate to follow suitable measures to prevent the landslides in the study area by involving all stockholders and with the active participation of local communities.     

Rainfall-induced landslide susceptibility zonation of Puerto Rico

Landslides are a major geologic hazard with estimated tens of deaths and $1–2 billion in economic losses per year in the US alone. The island of Puerto Rico experiences one or two large events per year, often triggered in steeply sloped areas by prolonged and heavy rainfall. Identifying areas susceptible to landslides thus has great potential value for Puerto Rico and would allow better management of its territory. Landslide susceptibility zonation (LSZ) procedures identify areas prone to failure based on the characteristics of past events. LSZs are here developed based on two widely applied methodologies: bivariate frequency ratio (FR method) and logistic regression (LR method). With these methodologies, the correlations among eight possible landslide-inducing factors over the island have been investigated in detail. Both methodologies indicate aspect, slope, elevation, geological discontinuities, and geology as highly significant landslide-inducing factors, together with land-cover for the FR method and distance from road for the LR method. The LR method is grounded in rigorous statistical testing and model building but did not improve results over the simpler FR method. Accordingly, the FR method has been selected to generate a landslide susceptibility map for Puerto Rico. The landslide susceptibility predictions were tested against previous landslide analyses and other landslide inventories. This independent evaluation demonstrated that the two methods are consistent with landslide susceptibility zonation from those earlier studies and showed this analysis to have resulted in a robust and verifiable landslide susceptibility zonation map for the whole island of Puerto Rico.     

Robust landslide susceptibility analysis by combination of frequency ratio,heuristic GIS-methods and ground truth evaluation for a mountainous study area with poor data availability in the Three Gorges Reservoir area,PR China

Increasing number of geohazards, like mass movements, is one of the main environmental impacts following the impoundment of the Yangtze River and its tributaries due to the inventory of the Three Gorges Dam hydroelectric power plant. Although many cities and settlements are endangered, no detailed hazard mapping is possible because of the huge size of the affected area. Due to strongly limited data availability, a robust landslide susceptibility model was established exemplarily for the Xiangxi catchment as one of the main tributaries. The analyses were limited to translational, rotational and combined landslides in soft rock sediments because these represent the main types of mass movement activity in this area. The qualitative landslide susceptibility analysis was carried out by a combination of frequency ratio analyses and a heuristic iterative index-based method using a Geographical Information System. As conditioning factors, the parameters lithology, slope angle, -aspect, -curvature, drainage buffer distance and land use were applied. To improve the objectivity of the index-based method, the results of frequency ratio analyses were taken into consideration to assess the importance of each factor. Model verification and evaluation by ground truth enable to improve the model by iterative calculations and to identify the best performance model. Results indicate that 89 % of all known landslides are located within areas showing high susceptibility according to the best performance model. The study demonstrates that a rather simple but robust model achieves good results and is applicable for regional landslide susceptibility analyses in mountainous areas with poor data availability.     

Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran

Globally, landslides cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year. A landslide susceptibility map describes areas where landslides are likely to occur in the future by correlating some of the principal factors that contribute to landslides with the past distribution of landslides. A case study is conducted in the mountainous northern Iran. In this study, a landslide susceptibility map of the study area was prepared using bivariate method with the help of the geographic information system. Area density (bivariate) method was used to weight landslide-influencing data layers. An overlay analysis is carried out by evaluating the layers obtained according to their weight and the landslide susceptibility map is produced. The study area was classified into five hazard classes: very low, low, moderate, high, and very high. The percentage distribution of landslide susceptibility degrees was calculated. It was found that about 26% of the study area is classified as very high and high hazard classes.     

Landslide Susceptibility Mapping Using Fuzzy Logic System and Its Influences on Mainlines in Lashgarak Region,Tehran, Iran

Landslide susceptibility mapping is among the useful tools applied in disaster management and planning development activities in mountainous areas. The susceptibility maps prepared in this research provide valuable information for landslide hazard management in Lashgarak region of Tehran. This study was conducted to, first, prepare landslide susceptibility maps for Lashgarak region and evaluate landslide effect on mainlines and, second, to analyze the main factors affecting landslide hazard increase in the study area in order to propose efficient strategies for landslide hazard mitigation. A GIS-based multi-criteria decision analysis model (fuzzy logic) is used in the present work for scientific evaluation of landslide susceptible areas in Lashgarak region. To this end, ArcGIS, PCIGeomatica, and IDIRISI software packages were used. Eight information layers were selected for information analysis: ground strength class, slope angle, terrain roughness, normalized difference moisture index, normalized difference vegetation index, distance from fault, distance from the river, and distance from the road. Next, eight different scenarios were created to determine landslide susceptibility of the study area using different operators (intersection (AND), union (OR), algebraic sum (SUM), multiplication (PRODUCT), and different fuzzy gamma values) of fuzzy overlay approach. After that, the performance of various fuzzy operators in landslide susceptibility mapping was empirically compared. The results revealed the excellent consistency of landslide susceptibility map prepared using the fuzzy union (OR) operator with landslide distribution map in the study area. Eventually, the accuracy of landslide susceptibility map prepared using the fuzzy union (OR) operator was evaluated using the frequency ratio diagram. The results showed that frequency values of the landslides gradually increase from “low susceptibility” to high “susceptibility” as 88.34% of the landslides are categorized into two “high” and “very high” susceptibility classes, implying the satisfactory consistency between the landslide susceptibility map prepared using fuzzy union (OR) operator and landslide distribution map.     

Regional assessment of landslide impact in the Three Gorges area, China, using ASTER data: Wushan-Zigui

This paper is focussed on the hazard impact of landslides in the Three Gorges, and represents the progression of our ongoing study on regional land instability assessment in the Three Gorges area using imagery data from the Advanced Spaceborne Thermal Emission Radiometer (ASTER). The key development here is the establishment of a model that integrates land instability with several factors that can relate hazard to human life, such as slope failures occurring in proximity to built-up areas and roads, and areas of high landslide risk along the bank of Yangtze and its major tributaries. The method correctly identifies some of the known destructive landslides in the region, like Qianjiangping and Huangtupo, as belonging to areas of potentially high landslide impact. Our results suggest that several population centres, including the towns of Wushan and Badong, are rated at high landslide hazard levels. This study highlights the importance of differentiating between landslide types within susceptibility assessment, and identifies those locations in the Three Gorges where the probability of landslide occurrence with negative impact to life and property is greatest.     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

Landslide hazard zoning along Himalayan Kaghan Valley of Pakistan��by integration of GPS, GIS, and remote sensing technology

This paper presents a methodology for developing a landslide hazard zonation map by integration of global positioning system (GPS), geographic information system (GIS), and remote sensing (RS) for Western Himalayan Kaghan Valley of Pakistan. The landslides in the study area have been located and mapped by using GPS. Eleven causative factors such as landuse, elevation, geology, rainfall intensity, slope inclination, soil, slope aspect, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams were analyzed for occurrence of landslides. These factors were used with a modified form of pixel-based information value model to obtain landslide hazard zones. The matrix analysis was performed in remote sensing to produce a landslide hazard zonation map. The causative factors with the highest effect of landslide occurrence were landuse, rainfall intensity, distances from main road, distances from secondary roads, and distances from main river and those from trunk streams. In conclusion, we found that landslide occurrence was only in moderate, high, or very high hazard zones, and no landslides were in low or very low hazard zones showing 100% accuracy of our results. The landslide hazard zonation map showed that the current main road of the valley was in the zones of high or very high hazard. Two new safe road routes were suggested by using the GIS technology.     

Influence of Shannon’s entropy on landslide-causing parameters for vulnerability study and zonation—a case study in Sikkim, India

Landslide is a common hazard in the hilly regions, which causes heavy losses to life and properties every year. Since 1980, various researches and analyses have been carried out in the geographic information systems (GIS) environment to identify factors responsible for causing landslides. The important conditioning factors identified by the researchers are slope, geological, geomorphologic structures, and land use coupled with triggering factors like rainfall and a few of the anthropogenic activities. Almost all landslides vulnerability studies carried out so far used parameters of landslide events of the past as essential inputs and advanced methods like information value, regression analysis, fuzzy logic, etc. The present research is an attempt to investigate the landslide vulnerabilities in different slope areas with simple and realistic method of assignments of weights to the parameters based on experts?? opinion and generic logic, without using the parameters of past landslide events as inputs. The identified factors were assigned appropriate weights based on experts?? opinion and these weights were further balanced with respect to the Shannon??s entropy of their occurrences within the study area. The study area was finally classified into three zones namely least vulnerable zone, moderately vulnerable zone, and most vulnerable zone. When compared with the actual landslide history of the past, it was found that Shannon??s entropy applied zonation model matched to real landslide events with higher value of landslide density as compared to the model developed without Shannon??s entropy.     

Comparative evaluation of landslide susceptibility in Minamata area, Japan

Landslides are unpredictable; however, the susceptibility of landslide occurrence can be assessed using qualitative and quantitative methods based on the technology of the Geographic Information Systems (GIS). A map of landslide inventory was obtained from the previous work in the Minamata area, the interpretation from aerial photographs taken in 1999 and 2002. A total of 160 landslides was identified in four periods. Following the construction of geospatial databases, including lithology, topography, soil deposits, land use, etc., the study documents the relationship between landslide hazard and the factors that affect the occurrence of landslides. Different methods, namely the logistic regression analysis and the information value model, were then adopted to produce susceptibility maps of landslide occurrence. After the application of each method, two resultant maps categorize the four classes of susceptibility as high, medium, low and very low. Both of them generated acceptable results as both classify the majority of the cells with landslide occurrence in high or medium susceptibility classes, which could be believed to be a success. By combining the hazard maps generated from both methods, the susceptibility was classified as high–medium and low–very low levels, in which the classification of high susceptibility level covers 6.5% of the area, while the areas predicted to be unstable, which are 50.5% of the total area, are classified as the low susceptibility level. However, comparing the results from both the approaches, 43% of the areas were misclassified, either from high–medium to low–very low or low–very low to high–medium classes. Due to the misclassification, 8% and 3.28% of all the areas, which should be stable or free of landsliding, were evaluated as high–medium susceptibility using the logistic regression analysis and the information value model, respectively. Moreover, in the case of the class rank change from high–medium susceptibility to low–very low, 35% and 39.72% of all mapping areas were predicted as stable using both the approaches, respectively, but in these areas landslides were likely to occur or were actually recognized.     

Shallow landslide hazard assessment using a three-dimensional deterministic model in a mountainous area

Shallow landslides are common in mountainous areas after intense rainfall. Of all landslide hazard assessment methods, deterministic methods provide the best quantitative information on landslide hazard. However, they require a large amount of detailed in situ data, derived from laboratory tests and field measurements, and therefore it is difficult to apply them over large areas. One of the most important input parameters is soil depth. For large areas, it is impossible to obtain soil depth through field measurements. To overcome this difficulty, a statistics-based regression analysis is used to evaluate soil depths. All the terrain attributes that control soil depths are selected as influential factors. By using multi-linear regression, the soil depths at each location can be predicted. Slope stability analysis can then be performed using deterministic methods with the evaluated soil depths. The study area is divided into slope units. For each slope unit, Monte-Carlo simulation and a GIS-based 3D limit equilibrium model are used to locate the critical slip surface and calculate the corresponding safety factor. The effectiveness of the proposed method has been tested by applying it to a mountainous area in Japan.