Landslide susceptibility mapping using the weight of evidence method in the Tinau watershed, Nepal

Mountainous areas in Nepal are prone to landslides, resulting in an enormous loss of life and property every year. As a first step towards mitigating or controlling such problems, it is necessary to prepare landslide susceptibility maps. Various methodologies have been proposed for landslide susceptibility mapping. This study applies the weight of evidence method to the Tinau watershed in west Nepal. A landslide susceptibility map is prepared on the basis of field observations and available data of geology, land use, topography and hydrology. Predicted susceptibility levels are found to be in good agreement with the locations of past landslides. The results show that about 30?% of the area is highly susceptible to landsliding. The present results provide useful information to the authorities concerning the landslide susceptibility zones and possible improvements for disaster management activities and sustainable development.     

Relationships among remotely sensed soil moisture,precipitation and landslide events

Landslides are triggered by earthquakes, volcanoes, floods, and heavy continuous rainfall. For most types of slope failure, soil moisture plays a critical role because increased pore water pressure reduces the soil strength and increases stress. However, in-situ soil moisture profiles are rarely measured. To establish the soil moisture and landslide relationship, a qualitative comparison among soil moisture derived from AMSR-E, precipitation from TRMM and major landslide events was conducted. This study shows that it is possible to estimate antecedent soil moisture conditions using AMSR-E and TRMM satellite data in landslide prone areas. AMSR-E data show distinct annual patterns of soil moisture that reflect observed rainfall patterns from TRMM. Results also show enhanced AMSR-E soil moisture and TRMM rainfall prior to major landslide events in landslide prone regions of California, U.S.; Leyte, Philippines; and Dhading, Nepal.     

基于频率比与集成学习的滑坡易发性评价——以金沙江上游巴塘—德格河段为例

金沙江上游巴塘—德格河段地处青藏高原东部,该区地质、地形、地貌极其复杂,滑坡灾害最为发育,开展区域滑坡易发性评价对防灾减灾工作有着重要的意义。本文以金沙江上游巴塘—德格河段为研究区,在滑坡编录与野外实际调查的基础上,通过对滑坡分布规律和影响因素分析,选取高程、坡度、坡向、曲率、地形起伏度、地表切割度、地表粗糙度、地层岩性、断层、水系和道路等11个影响因子,构建了滑坡易发性评价指标体系。利用皮尔森系数去除高相关性影响因子,运用频率比方法定量分析各个因子与滑坡发育的关系。通过频率比模型选取非滑坡样本,采用集成学习算法模型进行滑坡易发性评价,根据易发性指数将研究区划分为极高易发区、高易发区、中易发区、低易发区及极低易发区5个等级。由滑坡易发性分区图和ROC曲线表明,高和极高易发区主要沿金沙江沿岸和沟谷分布,随机森林模型的成功率曲线下面积AUC=0.84,历史滑坡灾害位于高-极高易发区的灾害数占总滑坡数的84.8%,梯度提升树模型的成功率曲线下面积AUC=0.79,历史滑坡灾害位于高-极高易发区灾害数占总滑坡数的79.3%。由AUC值和历史灾害的分布可知,随机森林模型比梯度提升树模型在本研究区...     

基于最大熵模型的中尼交通廊道滑坡易发性分析

中尼交通廊道作为中国近年来建设的重点区域,地质灾害频发,尤其是滑坡灾害层出不穷。文章基于对G216国道沿线地质灾害的实地调查以及遥感解译结果,以最大熵模型为方法,利用169个灾害点数据和8个评价因子图层预测了研究区滑坡灾害的易发性分布。根据占比划分五级风险区。结果表明,滑坡易发概率以G216为中心向外辐射逐渐降低。同时采用刀切法检验评价因子对预测结果的贡献度,确定了滑坡主导因素及其阈值。最后通过ROC曲线验证了模型的可靠性。为中尼边境公路区域建设提供一种地质灾害预测分析模型,也为青藏地区公路边坡防灾减灾提供有效支撑。     

Regional-scale landslide activity and landslide susceptibility zonation in the Nepal Himalaya

Landslide susceptibility zonation mapping is a fundamental procedure for geo-disaster management in tropical and sub-tropical regions. Recently, various landslide susceptibility zonation models have been introduced in Nepal with diverse approaches of assessment. However, validation is still a problem. Additionally, the role of various predisposing causative parameters for landslide activity is still not well understood in the Nepal Himalaya. To address these issues of susceptibility zonation and landslide activity, about 4,000 km² area of central Nepal was selected for regional-scale assessment of landslide activity and susceptibility zonation mapping. In total, 655 new landslides and 9,229 old landslides were identified with the study area with the help of satellite images, aerial photographs, field data and available reports. The old landslide inventory was “blind landslide database” and could not explain the particular rainfall event responsible for the particular landslide. But considering size of the landslide, blind landslide inventory was reclassified into two databases: short-duration high-intensity rainfall-induced landslide inventory and long-duration low-intensity rainfall-induced landslide inventory. These landslide inventory maps were considered as proxy maps of multiple rainfall event-based landslide inventories. Similarly, all 9,884 landslides were considered for the activity assessment of predisposing causative parameters. For the Nepal Himalaya, slope, slope aspect, geology and road construction activity (anthropogenic cause) were identified as most affective predisposing causative parameters for landslide activity. For susceptibility zonation, multivariate approach was considered and two proxy rainfall event-based landslide databases were used for the logistic regression modelling, while a relatively recent landslide database was used in validation. Two event-based susceptibility zonation maps were merged and rectified to prepare the final susceptibility zonation map and its prediction rate was found to be more than 82 %. From this work, it is concluded that rectification of susceptibility zonation map is very appropriate and reliable. The results of this research contribute to a significant improvement in landslide inventory preparation procedure, susceptibility zonation mapping approaches as well as role of various predisposing causative parameters for the landslide activity.     

GIS based landslide susceptibility mapping using a fuzzy logic approach: A case study from Ghurmi-Dhad Khola area, Eastern Nepal

Landslides cause extensive loss of life and property in the Nepal Himalaya. Since the late 1980s, different mathematical models have been developed and applied for landslide susceptibility mapping and hazard assessment in Nepal. The main goal of this paper is to apply fuzzy logic to landslide susceptibility mapping in the Ghurmi-Dhad Khola area, Eastern Nepal. Seven causative factors are considered: slope angle, slope aspect, distance from drainage, land use, geology, distance from faults and folds, soil and rock type. Likelihood ratios are obtained for each class of causative factors by comparison with past landslide occurrences. The ratios are normalized between zero and one to obtain fuzzy membership values. Further, different fuzzy operators are applied to generate landslide susceptibility maps. Comparison with the landslide inventory map reveals that the fuzzy gamma operator with a γ-value of 0.60 yields the best prediction accuracy. Consequently, this operator is used to produce the final landslide susceptibility zonation map.     

Improvement of statistical landslide susceptibility mapping by using spatial and global regression methods in the case of More and Romsdal (Norway)

Statistical models are one of the most preferred methods among many landslide susceptibility assessment methods. As landslide occurrences and influencing factors have spatial variations, global models like neural network or logistic regression (LR) ignore spatial dependence or autocorrelation characteristics of data between the observations in susceptibility assessment. However, to assess the probability of landslide within a specified period of time and within a given area, it is important to understand the spatial correlation between landslide occurrences and influencing factors. By including these relations, the predictive ability of the developed model increases. In this respect, spatial regression (SR) and geographically weighted regression (GWR) techniques, which consider spatial variability in the parameters, are proposed in this study for landslide hazard assessment to provide better realistic representations of landslide susceptibility. The proposed model was implemented to a case study area from More and Romsdal region of Norway. Topographic (morphometric) parameters (slope angle, slope aspect, curvature, plan, and profile curvatures), geological parameters (geological formations, tectonic uplift, and lineaments), land cover parameter (vegetation coverage), and triggering factor (precipitation) were considered as landslide influencing factors. These influencing factors together with past rock avalanche inventory in the study region were considered to obtain landslide susceptibility maps by using SR and LR models. The comparisons of susceptibility maps obtained from SR and LR show that SR models have higher predictive performance. In addition, the performances of SR and LR models at the local scale were investigated by finding the differences between GWR and SR and GWR and LR maps. These maps which can be named as comparison maps help to understand how the models estimate the coefficients at local scale. In this way, the regions where SR and LR models over or under estimate the landslide hazard potential were identified.     

Macro-zonation of landslide susceptibility in Garamaura-Swarghat-Gambhar section of national highway 21, Bilaspur District, Himachal Pradesh (India)

Landslides the most common geo-hazard in hilly terrain are short lived phenomena but cause extraordinary landscape changes and destruction of life and property. The frequency and intensity of landslides occurrences along NH-21 during the rainy season not only disrupts traffic movement but also misbalance the agro-economic and developmental activities of the region frittering away thousand crores of rupees from the exchequer. An assessment of landslide susceptibility is, therefore, a prerequisite for sustainable development of the region. The present study deals with the preparation of macro-zonation maps of landslide susceptibility in an area of about 100 sq km on 1:50,000 scale across Garamaura-Swarghat section of National Highway-21. The map has been prepared by superimposing the terrain evaluation maps in a particular zone such as lithological map, structural map, slope morphometry map, relative relief map, land use and land cover map and hydrological condition map using landslide susceptibility evaluation factor rating scheme and calculating the total estimated susceptibility as per the guidelines of IS: 14496 (Part-2) 1998). Numerical weightages are assigned to the prime causative factors of slope instability such as lithology, structure, slope morphometery, relative relief, land use and groundwater conditions as per the scheme approved by Bureau of Indian Standard for the purpose of landslide susceptibility zonation. The area depicts zones of different instability. The identified susceptibility zones compared with landslide intensity in the area show some congruence with the weightages of the inputs. The incongruence in intensity and frequency of landslide occurrences and the inferred susceptibility zones of BIS scheme allow other geotechnical considerations and causative factors to be incorporated for the landslide susceptibility zonation.     

樟木堆积体斜坡动力稳定性与极限承载力评价

樟木位于喜马拉雅山中段,是中国与尼泊尔的重要通商口岸,也是中尼公路的咽喉.特殊的地质地形条件和丰富的降水为地质灾害的发生提供了充分条件,区域内滑坡地质灾害发育.大量房屋建筑修建于樟木滑坡堆积体之上,滑坡灾害严重威胁到当地人民群众的生命财产安全和口岸经济的发展.目前,大量有关樟木滑坡堆积体稳定性的评价只考虑了暴雨条件和通...     

Application and validation of bivariate GIS-based landslide susceptibility assessment for the Vitravo river catchment (Calabria,south Italy)

The Calabria (Southern Italy) region is characterized by many geological hazards among which landslides, due to the geological, geomorphological, and climatic characteristics, constitute one of the major cause of significant and widespread damage. The present work aims to exploit a bivariate statistics-based approach for drafting a landslide susceptibility map in a specific scenario of the region (the Vitravo River catchment) to provide a useful and easy tool for future land planning. Landslides have been detected through air-photo interpretation and field surveys, by identifying both the landslide detachment zones (LDZ) and landslide bodies; a geospatial database of predisposing factors has been constructed using the ESRI ArcView 3.2 GIS. The landslide susceptibility has been assessed by computing the weighting values (Wi) for each class of the predisposing factors (lithology, proximity to fault and drainage line, land use, slope angle, aspect, plan curvature), thus evaluating the distribution of the landslide detachment zones within each class. The extracted predisposing factors maps have then been re-classified on the basis of the calculated weighting values (Wi) and by means of overlay processes. Finally, the landslide susceptibility map has been considered by five classes. It has been determined that a high percentage (61%) of the study area is characterized by a high to very high degree of susceptibility; clay and marly lithologies, and slope exceeding 20° in inclination would be much prone to landsliding. Furthermore, in order to ascertain the proposed landslide susceptibility estimate, a validation procedure has been carried out, by splitting the landslide detachment zones into two groups: a training and a validation set. By means of the training set, the susceptibility map has first been produced; then, it has been compared with the validation set. As a result, a great majority of LDZ-validation set (85%) would be located in highly and very highly susceptible areas. The predictive power of the model is considered reliable, since more than 50% of the LDZ fall into 20% of the most susceptible areas. The reliability of the susceptibility map is also suggested by computing the SCAI index, true positive and false positive rates; nevertheless, the most susceptible areas are overestimated. As a whole, the results indicate that landslide susceptibility assessment based on a bivariate statistics-based method in a GIS environment may be useful for land planning policy, especially when considering its cost/benefit ratio and the need of using an easy tool.     

Use of satellite remote sensing data in the mapping of global landslide susceptibility

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall.     

A comparative evaluation of heuristic and bivariate statistical modelling for landslide susceptibility mappings in Ghurmi–Dhad Khola, east Nepal

Landslides are one of the major natural disasters that occur in the Himalayan range with recurring frequency, causing enormous loss of life and property every year. Preparation of landslide inventory maps and landslide susceptibility zonation maps are the important tasks to be taken into account initially for safe mitigation measures. The present paper focuses on landslide susceptibility maps of the Ghurmi–Dhad Khola area, east Nepal, using Geographic Information System. For this purpose, the landslide susceptibility maps are prepared by using the heuristic and bivariate statistical methods. The parameters considered for the study are slope angle, slope aspect, elevation, distance from drainage, geology, land cover, rock and soil type, and distance from faults and folds. The landslide susceptibility zonation map produced from the heuristic method shows that 42.59 % of the observed landslide falls under the very high susceptible zone and 33.00 % under the high susceptible zone. Likewise, the landslide susceptibility zonation map produced from the bivariate method depicts that 44.19 % of the observed landslide falls under the very high susceptible zone and 31.59 % under the high susceptible zone. Both the landslide susceptibility zonation maps are identical, and success rates of both the maps are above 80 %. While comparing the landslide susceptibility maps obtained from two different methods, about 78 % of the study area falls in the identical susceptible zones. Special attention should be taken into consideration for the construction works in the areas which have been spatially agreed as very high and high susceptible zones from both techniques. Moreover, these maps can be used for slope management, land use planning, disaster management planning, etc., by the concerned authorities.     

Application of an analytical hierarchical process approach for landslide susceptibility mapping in A Luoi district,Thua Thien Hue Province,Vietnam

A Luoi is a Vietnamese–Laotian border district situated in the western part of Thua Thien Hue province, central Vietnam, where landslides occur frequently and seriously affect local living conditions. This study focuses on the spatial analysis of landslide susceptibility in this 263-km² area. To analyze landslide manifestation in the study area, causative factor maps are derived of slope angle, weathering, land use, geomorphology, fault density, geology, drainage distance, elevation, and precipitation. The analytical hierarchical process approach is used to combine these maps for landslide susceptibility mapping. A landslide susceptibility zonation map with four landslide susceptibility classes, i.e. low, moderate, high, and very high susceptibility for landsliding, is derived based on the correspondence with an inventory of observed landslides. The final map indicates that about 37% of the area is very highly susceptible for landsliding and about 22% is highly susceptible, which means that more than half of the area should be considered prone to landsliding.     

Susceptibility regional zonation of earthquake-induced landslides in Campania,Southern Italy

In this paper, we present a GIS-based method for regional zoning of seismic-induced landslide susceptibility and show its application to the territory of the Campania region, Southern Italy. The method employs only three factors that we believe are most significant in the susceptibility assessment: the type of outcropping rock/soil, the slope angle, and the MCS intensity. Each of the three parameters is quantified in terms of relative weight expressed as indices, and the resulting Seismic Landslide Susceptibility index of an area is given by the average of the indices of the first two factors multiplied by the index of the third factor. The result of this susceptibility zonation applied to Campania shows a good agreement between the distribution of the historical earthquake-triggered landslides and the highly susceptible zones.     

Shallow landslide susceptibility mapping using high-resolution topography for areas devastated by super typhoon Haiyan

Super typhoon Haiyan, considered as one of the most powerful storms recorded in 2013, devastated the central Philippines region on 8 November 2013 with damage amounting to more than USD 2 billion. Hardest hit is the province of Leyte which is located in central Philippines. Rehabilitation of the areas that were devastated requires detailed hazard maps as a basis for well-planned reconstruction. Along with severe wind, storm surge, and flood hazard maps, detailed landslide susceptibility maps for the cities and municipalities of Leyte (7246.7 km²) province are necessary. In order to rapidly assess and delineate areas susceptible to rainfall-induced shallow landslides, Stability INdex MAPping (SINMAP) software was used over a 5-m Interferometric Synthetic Aperture Radar (InSAR)-derived digital terrain model (DTM) grid. Topographic, soil strength, and hydrologic parameters were used for each pixel of a given DTM grid to compute for the corresponding factor of safety. The landslide maps generated using SINMAP are highly consistent with the landslide inventory derived from high-resolution satellite imagery from 2002 to 2014 with a detection percentage of 97.5 % and missing factor of 0.025. These demonstrate that SINMAP performs well despite the lack of an extensive geotechnical and hydrological database in the study area. The detailed landslide susceptibility classification is useful to identify safe and unsafe areas for reconstruction and rehabilitation efforts. These maps complement the debris flow and structurally controlled landslide hazard maps that are also being prepared for rebuilding Haiyan’s devastated areas.     

Three decades of landslide activity in western Nepal: new insights into trends and climate drivers

In recent decades, landslide disasters in the Himalayas, as in other mountain regions, are widely reported to have increased. While some studies have suggested a link to increasing heavy rainfall under a warmer climate, others pointed to anthropogenic influences on slope stability, and increasing exposure of people and assets located in harm’s way. A lack of sufficiently high-resolution regional landslide inventories, both spatially and temporally, has prevented any robust consensus so far. Focusing on Far-Western Nepal, we draw on remote sensing techniques to create a regional inventory of 26,350 single landslide events, of which 8778 date to the period 1992–2018. These events serve as a basis for the analyses of landslide frequency relationships and trends in relation to precipitation and temperature datasets. Results show a strong correlation between the annual number of shallow landslides and the accumulated monsoon precipitation (r = 0.74). Furthermore, warm and dry monsoons followed by especially rainy monsoons produce the highest incidence of shallow landslides (r = 0.77). However, we find strong spatial variability in the strength of these relationships, which is linked to recent demographic development in the region. This highlights the role of anthropogenic drivers, and in particular road cutting and land-use change, in amplifying the seasonal monsoon influence on slope stability. In parallel, the absence of any long-term trends in landslide activity, despite widely reported increase in landslide disasters, points strongly to increasing exposure of people and infrastructure as the main driver of landslide disasters in this region of Nepal. By contrast, no climate change signal is evident from the data.

     

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.     

Landslide susceptibility mapping using analytical hierarchy process (AHP) in Tehri reservoir rim region,Uttarakhand

A comprehensive use of analytical hierarchy process (AHP) method in landslide susceptibility mapping (LSM) has been presented for rim region of Tehri reservoir. Using remote sensing data, various landslide causative factors responsible for inducing instability in the area were derived. Ancillary data such as geological map, soil map, and topographic map were also considered along with remote sensing data. Exhaustive field checks were performed to define the credibility of the random landslide conditioning factors considered in this study. Apart from universally acceptable inherent causative factors used in the susceptibility mapping, others such as impact of reservoir impoundment on terrain, topographic wetness index and stream power index were found to be important causative factors in rim region of the Tehri reservoir. The AHP method was used to acquire weights of factors and their classes respectively. Weights achieved from AHP method matched with the existing field conditions. Acceptable consistency ratio (CR) value was achieved for each AHP matrix. Weights of each factor were integrated with weighted sum technique and a landslide susceptibility index map was generated. Jenk’s natural break classifier was used to classify LSI map into very low, low, moderate, high and very high landslide susceptible classes. Validation of the susceptibility map was performed using cumulative percentage/success rate curve technique. Area under curve value of the success rate curve was converted to percentage validation accuracy and a reasonable 78.7% validation accuracy was achieved.     

Landslide Susceptibility Zoning North of Yenice (NW Turkey) by Multivariate Statistical Techniques

Dramatic effects resulting from landslides on human life and economy of many nations are observed sometimes throughout the world. Landslide inventory and susceptibility mapping studies are accepted as the first stage of landslide hazard mitigation efforts. Generally, these landslide inventory studies include identification and location of landslides. The main benefit is to provide a basis for statistical susceptibility zoning studies. In the present study, a landslide susceptibility zoning near Yenice (NW Turkey) is carried out using the factor analysis approach. The study area is approximately 64 km² and 57 landslides were identified in this area. The area is covered completely by Ulus Formation that has a flysh-like character. Slope angle, elevation, slope aspect, land-use, weathering depth and water conditions were considered as the main conditioning factors while the heavy precipitation is the main trigger for landsliding. According to the results of factor analysis, the importance weights for slope angle, land-use, elevation, dip direction, water conditions and weathering depth were determined as 45.2%, 22.4%, 12.5%, 8.8%, 8.1% and 3.0% respectively. Also, using these weights and the membership values of each conditioning factor, the membership value for landslide susceptibility was introduced. In the study area, the lowest membership value for landslide susceptibility was calculated as 0.20. Consequently, combining all results, a landslide susceptibility map was obtained. Compared with the obtained map, a great majority of the landslides (86 %) identified in the field were found to be located in susceptible and highly susceptible zones.     

基于证据权模型的川西鲜水河断裂带滑坡易发性评价

鲜水河断裂带是发育于青藏高原东缘的一条大型左旋走滑断裂带,该区新构造活动强烈且历史强震频发,一系列大型-巨型滑坡沿断裂带密集分布。在资料收集的基础上,对鲜水河断裂带两侧10 km区域内进行遥感解译和野外地质调查,建立数据库并对滑坡主要影响因素进行分析。在滑坡区域发育分布规律分析的基础上,选取地形坡度、地形坡向、地面高程、平面曲率、地形湿度指数、活动断裂、工程地质岩组、年降雨量、河流、道路、植被覆盖指数等11个因素作为滑坡易发性评价因子,在ArcGIS软件平台上,采用证据权模型开展了滑坡易发性评价。根据成功率曲线对评价结果的检验,滑坡易发性评价结果具有较好的精度,并将研究区的滑坡易发程度划分为极高易发、高易发、中等易发、低易发和不易发5个级别。滑坡的易发性受鲜水河断裂带影响显著,极高易发区和高易发区主要分布在东谷到道孚县沿鲜水河断裂带两侧,以及康定县城和磨西镇附近;中等易发区主要分布在鲜水河支流两岸及省道沿线;滑坡低易发区和不易发区主要分布在人类工程活动少的高山地带以及地形相对平缓的区域。滑坡易发性评价结果很好地反映了鲜水河断裂带区域内滑坡发育分布现状,为该区重大工程规划建设和防灾减灾提供参考依据。