Estimation of landslide risk map considering landslide vulnerability: Case of Algerian Western coasts

Abstract. Vulnerability identifies the element-at-risk as well as the evaluation of their relationships with the hazard. The relationships relate the landslide potential damages over a specific element-at-risk. Vulnerability can be defined as the degree of loss to a given element-at-risk or set of elements at risk resulting from the occurrence of a natural phenomenon of a given magnitude and expressed on a scale from 0 (no damage) to 1 (total damage). In this study, the landslide vulnerability mapping and analysis were made on two element-at-risks namely buildings and roads. Based on field observations, building and road construction materials have been classified into 22 and 5 construction materials respectively. The field visits were made on specific areas based on candidate buildings and roads as chosen during the landslide exposure analysis and mapping. The vulnerability values for these element-at-risks were expressed using expert opinion. Four experts have been interviewed with separate sessions. The experts were also supplied with local information on the landslides occurrences and photos of element-at-risk in Kundasang and Kota Kinabalu. The vulnerability matrices were combined based on the weighted average approach, in which higher weight was assigned to panel with local expert (landslides and damage assessment), wide experience in landslide vulnerability analysis, hazard and risk mapping. Finally, the vulnerability maps were produced for Kundasang and Kota Kinabalu with spatial resolution of 25 cm. These maps were used for the next step i.e. landslide risk mapping and analysis.

This research paper assesses the vulnerability of landslide for the Bodi-Bodimettu Ghat section, Theni district, Tamil Nadu, India, using remotely sensed data and geographic information system (GIS). Landslide database was generated using IRS-1C satellite LISS III data and aerial photographs accompanied by field investigations using differential global positioning system to generate a landslide inventory map. Topographical, spatial, and field data were processed to construct the spatial thematic layers using image processing and GIS environment. Twelve landslide-inducing factors were used for landslide vulnerability analysis: elevation, slope, aspect, plan curvature, profile curvature, proximity to road, drainage and lineament, land use/land cover, geology, geomorphology, and run-off. The first five factors were derived from digital elevation model, and other thematic layers were prepared from spatial database. Frequency ratio of each factor was computed using the above thematic factors with past landslide locations. Landslide vulnerability map was produced using raster analysis. The landslide vulnerability map was classified into five zones: very low, low, moderate, high, and very high. The model is validated using the relative landslide density index (R-index method). The consistency of R-index indicates good performance of the vulnerability map.

Landslide hazard, vulnerability, and risk-zoning maps are considered in the decision-making process that involves land use/land cover (LULC) planning in disaster-prone areas. The accuracy of these analyses is directly related to the quality of spatial data needed and methods employed to obtain such data. In this study, we produced a landslide inventory map that depicts 164 landslide locations using high-resolution airborne laser scanning data. The landslide inventory data were randomly divided into a training dataset: 70 % for training the models and 30 % for validation. In the initial step, a susceptibility map was developed using logistic regression approach in which weights were assigned to every conditioning factor. A high-resolution airborne laser scanning data (LiDAR) was used to derive the landslide conditioning factors for the spatial prediction of landslide hazard areas. The resultant susceptibility was validated using the area under the curve method. The validation result showed 86.22 and 84.87 % success and prediction rates, respectively. In the second stage, a landslide hazard map was produced using precipitation data for 15 years. The precipitation maps were subsequently prepared and show two main categories (two temporal probabilities) for the study area (the average for any day in a year and abnormal intensity recorded in any day for 15 years) and three return periods (15-, 10-, and 5-year periods). Hazard assessment was performed for the entire study area. In the third step, an element at risk map was prepared using LULC, which was considered in the vulnerability assessment. A vulnerability map was derived according to the following criteria: cost, time required for reconstruction, relative risk of landslide, risk to population, and general effect to certain damage. These criteria were applied only on the LULC of the study area because of lack of data on the population and building footprint and types. Finally, risk maps were produced using the derived vulnerability and hazard information. Thereafter, a risk analysis was conducted. The LULC map was cross-matched with the results of the hazard maps for the return period, and the losses were aggregated for the LULC. Then, the losses were calculated for the three return periods. The map of the risk areas may assist planners in overall landslide hazard management.

Weighted linear combination (WLC) method was used to assess landslides vulnerability of the Simbu Province, Papua New Guinea within the GIS environment of ArcGIS. This multi-criteria evaluation method allows flexibility and tradeoffs amongst all parameters used. Ranks and weights are assigned depending on their influence on the occurrence of landslides. Parameters selected for the study include slope angle, elevation, rainfall, vegetation cover, land use/land cover, landform, proximity to roads, proximity to rivers and proximity to lineaments. Restricted in some sense in terms of data, WLC was appropriate in using existing metadata of the country; Papua New Guinea Resource Information System and Forest Information Management System. The landslide susceptibility map provides valuable information of the risk at hand in the province and district levels to better manage and plan mitigation measures. The slope factor was assigned a weighted of 4 as having greater influence on landslides in the region followed by rainfall weighted of 2 and the other having uniform influence of 1. The study area shows the distribution of the five vulnerability/susceptibility classes ranking from very low (1) to very high (5). Areas with very high landslide vulnerability zones are found in the northern and western parts of Simbu Province. Comparatively, southern parts have very low vulnerability areas. From the calculations done, 6.21 % of area is at very low risk, 20.24 % at low risk, 32.27 % of moderate risk, 26.88 % of high risk and 14.41 % of very high risk area coverage.

Landslides are major natural hazards that have a wide impact on human life, property, and natural environment. This study is intended to provide an improved framework for the assessment of landslide vulnerability mapping (LVM) in Chukha Dzongkhags (district) of Bhutan. Both physical (22 nos.) and social (9 nos.) conditioning factors were considered to model vulnerability using deep learning neural network (DLNN), artificial neural network (ANN) and convolution neural network (CNN) approaches. Selection of the factors was conceded by the collinearity test and information gain ratio. Using Google Earth images, official data, and field inquiry a total of 350 (present and historical) landslides were recorded and training and validation sets were prepared following the 70:30 ratio. Nine LVMs were produced i.e. a landslide susceptibility (LS), one social vulnerability (SV) and a relative vulnerability (RLV) map for each model. The performance of the models was evaluated by area under curve (AUC) of receiver operating characteristics (ROC), relative landslide density index (R-index) and different statistical measures. The combined vulnerability map of social and physical factors using CNN (CNN-RLV) had the highest goodness-of-fit and excellent performance (AUC = 0.921, 0.928) followed by DLNN and ANN models. This approach of combined physical and social factors create an appropriate and more accurate LVM that may—support landslide prediction and management.

Mapping of landslide hazard in North Sumatera is very important to identify the areas potential. In this study we try to predict and mapping of landslide hazard in North Sumatera using scoring and GIS. Compare to the ground movement vulnerability and rainfall series, the areas with steep topography have landslide hazard forecasts, which are dominated by zones with medium hazard forecasts. High-level landslide hazard forecast maps are in the areas of South Tapanuli, Mandailing Natal, Humbang Hasundutan, Central Tapanuli, North Tapanuli, South Nias, West Nias, Padang Lawas, Toba Samosir, Langkat, Samosir, Pakpak Bharat, and Karo districts.

Introduction: Landslides are one of the critical natural processes, which cause enormous damage to life and property..Uttarakhand Himalaya is vital to sustainity of region. This region is very fragile due to topographic and climatic condition. Landslides in the mountainous terrains are natural derivational processes and one of the most. There is a need for Landslide Hazard Zonation for identification of potential landslide areas. Keeping this in mind one hilly district, Pauri district of Uttarakhand, India was selected. Materials and Methods: The present study is an attempt towards development of a landslide model by using multi-criteria decision analysis in GIS and remote sensing techniques for landslide hazard zonation. The IRS LISS-4 satellite imageries, Survey of India topographical maps, and ancillary data were used as inputs to the study. The data layers of land use–land cover and geology were interpreted from satellite image and available ancillary data. Other raster thematic layers, i.e., slope, aspect, elevation, and drainage density, have been generated in Arc info three-dimensional analyst tool using ASTER DEM of 30 m resolution. A numerical rating scheme for the factors was developed for spatial data analysis in GIS. Results and Decision: Using high-resolution LISS-IV and Cartosat-2A, merged data of 2011–2012 landslides were mapped by visual interpretation of satellite image. A total of 167 landslides have been identified. Elevation map of Pauri district was prepared by classifying digital elevation model into 5 elevation categories. Distribution of landslides in different elevation categories is also done. Maximum landslides have occurred in elevation category (710–1134 m) and (185–709 m). Slope map has been generated and classifi ed into 6 slope classes, i.e. 0°–5°, 5°–10°, 10°–20°, 20°–30°, 30°–40°, and more than 40°. Distribution of landslides in different slope categories is also done. Maximum landslides have occurred in slopecategories (10°–20°), (20°–30°), and (30°–40°). Aspect map has been generated and classified into 8 aspect classes. Maximum landslides have occurred in aspect categories South-West (14.37%), South (35.97%), and South East (15.57%) directions. The Southern aspect (SW-S-SE) contributes to 65.91% of total landslides. Drainage density map has been generated from ASTER DEM using three-dimensional Analyst tool and classified into 6 classes, namely 0–2, 2–4, 4–6, 6–8, and more than 8 (km/sq.km). Geological map of Pauri district has been adopted from Geological map from Geological SOI. Rock types such as phyllites, quartzites, slates, siltstone, sandstones, graywacks, granitoids, alluvium, crystallines and metamorphics, and limestones are present in the district. Distribution of landslides in different drainage density categories is also done. Maximum landslides have occurred in drainage density category 2–4, 4–6, and 6–8 (km/sq.km). LULC map of Pauri was classified into various classes such as water body, forest scrub, wasteland/scrub, tree-clad, forest evergreen, agriculture land, built up, forest deciduous open, and forest deciduous dense/close. Maximum landslides have occurred in forest deciduous open category (84) followed by agriculture land (22), forest scrub (14), forest evergreen and forest deciduous dense/close (13 each), and water body. Conclusion: The above-mentioned 6 layers were integrated in the weighted overlay model of Arc Info and weights were assigned corresponding to landslide occurrence in these layers. The influence of the layer in causing landslide has also assigned. The final LHZ map generated after processing 6 themes. The final map has been classified into 3 categories, namely high, moderate, and low hazard categories. Of a total of 167 landslides, 110Landslides fall in the high HZ and 43 landslides fall in moderate HZ category while only 14 landslides fall in the low HZ category. 34.01% of the district falls under low HZ category and 40.73% falls under moderate HZ category while 25.26 under high HZ category. More layers need to be added in the model to obtain more precision. This map is not checked in the fi eld ground truth.

Landslides are among hazards that undermine the social, economic, and environmental well-being of the vulnerable community. Assessment of landslides vulnerability reveals the damages that could be recorded, estimates the severity of the impact, and increases the preparedness, response, recovery, and mitigation as well. This study aims to estimate landslides vulnerability for the western province of Rwanda. Field survey and secondary data sources identified 96 landslides used to prepare a landslides inventory map. Ten factors-altitude, slope angles, normalized difference vegetation index (NVDI), land use, distance to roads, soil texture, rainfall, lithology, population density, and possession rate of communication tools-were analyzed. The Analytical Hierarchy Process (AHP) model was used to weight and rank the vulnerability conditioning factors. Then the Weighted Linear Combination (WLC) in geographic information system (GIS) spatially estimated landslides vulnerability over the study area. The results indicated the altitude (19.7%), slope angles (16.1%), soil texture (14.3%), lithology (13.5%), and rainfall (12.2%) as the major vulnerability conditioning parameters. The produced landslides vulnerability map is divided into 5 classes: very low, low, moderate, high and very high. The proposed method is validated by using the relative landslides density index (R-index) method, which revealed that 35.4%, 25%, and 23.9% of past landslides are observed within moderate, high, and very high vulnerability zones, respectively. The consistency of validation indicates good performance of the methodology used and the vulnerability map prepared. The results can be used by policy makers to recognize hazard vulnerability lessening and future planning needs. Integr Environ Assess Manag 2019;00:000-000. © 2019 SETAC.

In order to study landslides using GIS, it is first necessary to identify landslides based on their characteristics: deep-seated landslides, such as main scarps, debris, mounds, and hollows, or shallow landslides. Deep-seated landslides are classified into rotational slide (slump), planar slide (glide), debris avalanche, or earth flows. Shallow landslides are composed of scar, flow, and deposit part. The seat is classified into planar or spoon type. After producing an inventory of landslides, an analog map should be transformed into a digital map and analyzed using GIS to obtain “landslide hazard maps” including: (1) landslide inventory maps, (2) landslide susceptibility maps, (3) landslide hazard maps, and (4) landslide risk maps. Finally, this chapter reviews on GIS landslide analyses and susceptibility mapping reviews briefly representative papers.

GCI(Geospatial Correlative Integration) 중 Frequency ratio 모델을 적용하여 2006년 태풍 에위니아에 의하여 발생한 강원도 인제 가리산리 지역의 산사태 취약성도를 작성하였다. 또한 연구지역의 미래 확률강우량을 적용하여 미래 목표연도별 산사태 취약성도를 작성하였다. 산사태 취약성와 관련된 요인으로는 기후노출은 확률강우량, 민감도는 지질, 지형도(경사, 경사방향, 곡률도), 토양도(토양 지형, 토질, 토양 배수, 토양 모재 및 유효토심) 및 적응능력으로 는 임상도(영급, 경급, 소밀도 및 수종) 등을 GIS 기반의 공간 데이터베이스로 구축하였다. 전체 산사태 발생 위치는 470개소이며 이 중 50%는 Frequency ratio 및 Neural network 모델의 산사태 발생 지역으로 적용하였으며, 나머지 50%는 취약성도 검증에 활용하였다. 산사태 발생 강우량 임계치는 3일 누적 449mm로 적용하였다. 확률강우량은 1973년부터 2006년까지의 실측 강우량을 정리하여 2106년까지 목표연도별(1년, 3년, 10년, 50년 및 100년) 산사태 취약 성도를 작성하였다. 연구결과 연구지역은 경사도가 다른 항목에 비하여 산사태 발생에 높은 가중치를 나타냈으며 경 사도 항목의 세부 항목의 산사태 발생과의 상관관계에서는 경사도 25~30°인 지역이 높은 상관관계를 나타냈다. 또한 강우량의 증가에 의하여 미래 산사태 발생 가능성이 매우 높은 지역이 2006년을 기준으로 2100년까지 약 110% 증가하였 다. 앞으로 강우량의 변화에 의한 산사태 위험성 분석에 한 축을 차지할 수 있다는 점에서 중요성이 있다. 기존에 확률강우량 변화에 따른 산사태 발생의 불명확한 관계를 정량적으로 분석하였으며, 미래 기후변화 예측 결과를 반영한 연구지역 내 산사태 발생 변화를 시-공간적으로 산정하고, 기존 산정 결과와의 비교를 통해, 향후 기후 변화를 고려한 국내 산사태 관리 방안 수립을 위한 방향을 제시하기 위한 연구라고 할 수 있다. 앞으로 분석모델의 고도화 방안 및 현장조사가 추가된다면 보다 정량적으로 기후변화와 산사태의 상관관계를 파악할 수 있으며, 산사태 의 전반적인 관리 및 효율적인 운영 체제 구축을 위하여 기여할 수 있다는 점에서 그 중요성이 있다.

The flood events are now intense because of localized physical and climatic factors, resulting in risks to the environment and the population. Besides, the difficult to identify the flood vulnerable area at Pasir Mas, Kelantan had become the main problem because lack of comprehensive information which as a medium to communicating the flood risk, new unplanned developments and insufficient drainage systems. Hence, the development of a vulnerability map for flood risk of flooding management still lacks, which made the situation more considerable. Vulnerability is the primary construct in flood risk management. Therefore, this study aims to identify the variables which contribute to the risk of flooding based on the characteristics of the area and develop a flood vulnerability map using Geographical Information Systems (GIS) and Remote Sensing. In this study, the land use data, amount average of rainfall data and digital elevation model (DEM) data were used to produce a vulnerable flood map for the study area. The hydrology and weight overlay (spatial analyst) techniques were used to determine the flood vulnerable area on physical and climatic factors that cause flooding in Pasir Mas and to develop the vulnerable flood map in Pasir Mas. The vulnerability area had been determined based on the scale 1 (no vulnerability), 2 (low vulnerability), 3 (reasonable vulnerability), 4 (moderate vulnerability) and 5 (high vulnerability). The findings show that the area located at the water bodies recorded the most highly vulnerable compared to another area because water bodies store the water. When surface water run-off from the surrounding area exceeds the level of water bodies, it increases the flow capacity of the water and causes the flood. From here, it makes the water bodies and area surrounded it more vulnerable. It is expected within this vulnerable flood map will able to assist the responsible parties to communicate and give an option to those affected people to ensure the effectiveness of the emergency response assistance and aid to victims for better preparedness capability.

The use of preevent and postevent digital elevation models (DEMs) to estimate the volume of rock avalanches on glaciers is complicated by ablation of ice before and after the rock avalanche, scour of material during rock avalanche emplacement, and postevent ablation and compaction of the rock avalanche deposit. We present a model to account for these processes in volume estimates of rock avalanches on glaciers. We applied our model by calculating the volume of the 28 June 2016 Lamplugh rock avalanche in Glacier Bay National Park, Alaska. We derived preevent and postevent 2‐m resolution DEMs from WorldView satellite stereo imagery. Using data from DEM differencing, we reconstructed the rock avalanche and adjacent surfaces at the time of occurrence by accounting for elevation changes due to ablation and scour of the ice surface, and postevent deposit changes. We accounted for uncertainties in our DEMs through precise coregistration and an assessment of relative elevation accuracy in bedrock control areas. The rock avalanche initially displaced 51.7 ± 1.5 Mm3of intact rock and then scoured and entrained 13.2 ± 2.2 Mm3of snow and ice during emplacement. We calculated the total deposit volume to be 69.9 ± 7.9 Mm3. Volume estimates that did not account for topographic changes due to ablation, scour, and compaction underestimated the deposit volume by 31.0–46.8 Mm3. Our model provides an improved framework for estimating uncertainties affecting rock avalanche volume measurements in glacial environments. These improvements can contribute to advances in the understanding of rock avalanche hazards and dynamics.

Effects of soil depth and subsurface flow along the subsurface topography on shallow landslide predictions at the site of a small granitic hillslope

Landslide is one of geomorphology process that can be a natural hazard for humans. An area has internal parameters that can cause landslide occurrences such as slope, lithology, rainfall, and hydrology. Furthermore, human activities can increase susceptibility for the landslide. Thus, the landslide vulnerability mapping is required to obtain potential of landslide susceptibility of an area. The study area is located in the Boja District. The goals are to determine the weight of internal factors which can cause vulnerable for landslide and to develop vulnerable landslide zone. The methods were remote sensing analysis using Digital Elevation Model (DEM) data and Analytical Hierarchy Process (AHP) to define the weight of internal parameters which cause a landslide. The results show that there are three levels of the vulnerable zone i.e. low, moderate, and high vulnerable levels of the landslide.

Road constructions, especially in mountainous topographical features, have been noted responsible for the significant increase of landslide occurrences. In the Garut – Tasikmalaya Road, numerous landslides have been recorded causing enormous losses. This road has a very important role as the main transportation connector between two regencies in West Java; those are Garut and Tasikmalaya Regency. To deal and control such problems, spatial distributions of landslide vulnerability need to be analyzed. The aim of this study was to assess the widespread landslide vulnerability zones along the Garut – Tasikmalaya Road. The landslide triggering factors, i.e. topographic slope, land use, lithology, distance to geological structure and distance to stream, were spatially evaluated and integrated using Analytical Hierarchy Process (AHP) and simple numerical rating method to determine the landslide vulnerability zones. The data of landslide triggering factors were derived from remote sensing data and processed using GIS technology. The data integration produced landslide vulnerability map along the Garut – Tasikmalaya Road, which delineates the research area into three zones of landslide vulnerability (high, moderate, and low). The landslide vulnerability map was validated using landslide inventory. The high vulnerability zones of landslide, that will affect the Garut – Tasikmalaya Road, are mainly located in the Salawu District.