Geo-spatial Technology for Landslide Hazard Zonation and Prediction

Similar to other geo hazards, landslides cannot be avoided in mountainous terrain. It is the most common natural hazard in the mountain regions and can result in enormous damage to both property and life every year. Better understanding of the hazard will help people to live in harmony with the pristine nature. Since India has 15% of its land area prone to landslides, preparation of landslide susceptibility zonation (LSZ) maps for these areas is of utmost importance. These susceptibility zonation maps will give the areas that are prone to landslides and the safe areas, which in-turn help the administrators for safer planning and future development activities. There are various methods for the preparation of LSZ maps such as based on Fuzzy logic, Artificial Neural Network, Discriminant Analysis, Direct Mapping, Regression Analysis, Neuro-Fuzzy approach and other techniques. These different approaches apply different rating system and the weights, which are area and factors dependent. Therefore, these weights and ratings play a vital role in the preparation of susceptibility maps using any of the approach. However, one technique that gives very high accuracy in certain might not be applicable to other parts of the world due to change in various factors, weights and ratings. Hence, only one method cannot be suggested to be applied in any other terrain. Therefore, an understanding of these approaches, factors and weights needs to be enhanced so that their execution in Geographic Information System (GIS) environment could give better results and yield actual ground like scenarios for landslide susceptibility mapping. Hence, the available and applicable approaches are discussed in this chapter along with detailed account of the literature survey in the areas of LSZ mapping. Also a case study of Garhwal area where Support Vector Machine (SVM) technique is used for preparing LSZ is also given. These LSZ maps will also be an important input for preparing the risk assessment of LSZ

Integrating expert knowledge with statistical analysis for landslide susceptibility assessment at regional scale

Abstract: In this paper, an integration landslide susceptibility model by combining expert-based and bivariate statistical analysis (Landslide Susceptibility Index—LSI) approaches is presented. Factors related with the occurrence of landslides—such as elevation, slope angle, slope aspect, lithology, land cover, Mean Annual Precipitation (MAP) and Peak Ground Acceleration (PGA)—were analyzed within a GIS environment. This integrated model produced a landslide susceptibility map which categorized the study area according to the probability level of landslide occurrence. The accuracy of the final map was evaluated by Receiver Operating Characteristics (ROC) analysis depending on an independent (validation) dataset of landslide events. The prediction ability was found to be 76% revealing that the integration of statistical analysis with human expertise can provide an acceptable landslide susceptibility assessment at regional scale

Test of Fuzzy Logic Rules for Landslide Susceptibility Assessment

16 p.International audienceLandslide Susceptibility Assessment (LSA) is defined as the spatial probability for a landslide to be generated in an area for many environmental factors. Currently, two approaches are used: (i) the qualitative approach based on expert opinion and knowledge of the relationship between the observed phenomenon and some predisposing factors and (ii) the statistical approach based on the statistical analysis of the relationship between the observed landslide and some predisposing factors. This paper proposes an exploratory attempt to use Fuzzy Logic Rules for mapping landslide susceptibility. The technique allows to describe the role of each predisposing factor (predictive variable) and their optimal combination. The best predictive variables identified by Fuzzy Logic are then introduced in a statistical bivariate model. The simulated maps obtained by both approaches are then compared and evaluated with an expert map, build with the prescribed rules of the French PPR (Plan de Prévention des Risques) methodology, and considered as a map of reference

Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling-Narayanghat road section in Nepal Himalaya

Landslide susceptibility maps are vital for disaster management and for planning development activities in the mountainous country like Nepal. In the present study, landslide susceptibility assessment of Mugling-Narayanghat road and its surrounding area is made using bivariate (certainty factor and index of entropy) and multivariate (logistic regression) models. At first, a landslide inventory map was prepared using earlier reports and aerial photographs as well as by carrying out field survey. As a result, 321 landslides were mapped and out of which 241 (75 %) were randomly selected for building landslide susceptibility models, while the remaining 80 (25 %) were used for validating the models. The effectiveness of landslide susceptibility assessment using GIS and statistics is based on appropriate selection of the factors which play a dominant role in slope stability. In this case study, the following landslide conditioning factors were evaluated: slope gradient; slope aspect; altitude; plan curvature; lithology; land use; distance from faults, rivers and roads; topographic wetness index; stream power index; and sediment transport index. These factors were prepared from topographic map, drainage map, road map, and the geological map. Finally, the validation of landslide susceptibility map was carried out using receiver operating characteristic (ROC) curves. The ROC plot estimation results showed that the susceptibility map using index of entropy model with AUC value of 0.9016 has highest prediction accuracy of 90.16 %. Similarly, the susceptibility maps produced using logistic regression model and certainty factor model showed 86.29 and 83.57 % of prediction accuracy, respectively. Furthermore, the ROC plot showed that the success rate of all the three models performed more than 80 % accuracy (i.e. 89.15 % for IOE model, 89.10 % for LR model and 87.21 % for CF model). Hence, it is concluded that all the models employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of Mugling-Narayanghat road section. These landslide susceptibility maps can be used for preliminary land use planning and hazard mitigation purpose.ArticleNATURAL HAZARDS. 65(1):135-165 (2013)journal articl

Morphological parameters causing landslides: A case study of elevation

The history of landslide susceptibility maps goes back about 50 years. Hazard and risk maps later followed these maps. Inventory maps provide the source of all these. There are different parameters selected specially for each field in the literature as well as parameters selected because they are easy to produce and obtain data. This study tried to research the effect of elevation on landslides by reviewing the literature in detail. The used class ranges and elevation values were reviewed and applied to map sections selected from Turkey. By analyzing the results, the goal was to determine at which elevation ranges landslides occurred. The study tried to investigate the effect of the parameter of elevation using data from the literature. It works to compare the elevation values for map sections selected to compare with the literature. The study comprises two stages. The first step tried to acquire statistical data by researching the data from the literature. The data were investigated in the second stage. For this purpose, close to 1.500 studies prepared between 1967 and 2019 were reviewed. According to the literature, the parameter of was used in analyses because it is easy to produce and is morphologically effective

Análise multi-critério aplicada ao mapeamento da suscetibilidade a escorregamentos

This paper presents the application of a multi-criteria analysis (MCA) tool for landslide susceptibility assessment in Porto Alegre municipality, southern Brazil. A knowledge driven approach was used, aiming to ensure an optimal use of the available information. The landslide conditioning factors considered were slope, lithology, fl ow accumulation and distance from lineaments. Standardization of these factors was done through fuzzy membership functions, and evaluation of their relative importance for landslide predisposition was supported by the analytic hierarchy process (AHP), based on local expert knowledge. Finally, factors were integrated in a GIS environment using the weighted linear combination (WLC) method. For validation, an inventory, including 107 landslide points recorded between 2007 and 2013 was used. Results indicated that 8.2% (39.40 km²) of the study area are highly and very highly susceptible to landslides. An overall accuracy of 95% was found, with an area under the receiver operating characteristic (ROC) curve of 0.960. Therefore, the resulting map can be regarded as useful for monitoring landslide-prone areas. Based on the fi ndings, it is concluded that the proposed method is eff ective for susceptibility assessment since it yielded meaningful results and does not require extensive input data.Este estudo apresenta a aplicação de uma ferramenta de análise multi-critério para mapear a suscetibilidade a escorregamentos no município de Porto Alegre, sul do Brasil. Uma abordagem guiada pelo conhecimento de especialistas foi utilizada, com o intuito de otimizar a utilização das informações disponíveis. Os fatores condicionantes dos escorregamentos considerados foram declividade, litologia, acúmulo defl uxo e distância de lineamentos. A padronização desses fatores foi realizada por meio da aplicação de funções fuzzy e a importância relativa de cada um na predisposição do terreno a escorregamentos foi estabelecida com o apoio da técnica AHP (Analytic Hierarchy Process), com base no conhecimento de especialistas locais. Por fi m, a integração dos fatores em ambiente SIG se deu por meio do método denominado Combinação Linear Ponderada (WLC). Para validar os resultados, utilizou-se um mapa inventário contendo 107 cicatrizes de escorregamentos, registradas entre 2007 e 2013. Os resultados indicam que 8,2% (39,38 km²) da área de estudo possui uma suscetibilidade alta e muito alta a escorregamentos. A validação dos resultados obteve uma exatidão geral de 95%, com uma área abaixo da curva ROC (Receiver Operating Characteristic) de 0,960. Portanto, o mapa obtido pode ser considerado útil para monitorar as áreas propensas a esses processos. Com base nos resultados, conclui-se que o método proposto é efi caz para a avaliação da suscetibilidade, uma vez que os resultados obtidos são robustos e que não foi necessária uma quantidade extensa de dados de entrada

Landslide Hazard Mapping of Bagh District in Azad Kashmir

Landslide is a process which comprises creep movement, flow movement, rockfalls, snow avalanche and soil flow on a slope. Human beings in hilly regions face catastrophe in the form of land sliding specifically during rain time period. This paper will inspect the study of landslide hazard areas detection from NASA’s Shuttle Radar Topography Mission (SRTM)’s Digital Elevation Model and moderate resolution Landsat-8 imagery over the district of Bagh in Azad Kashmir by applying hazard evaluation technique of average weighted overlay method with 6 factors in GIS environment. The outcome of this calculation is an area in the form of hazardous zones in eastern, north-eastern and south eastern of Bagh district in AJK. Landsat-8 imagery is considered effective for observing topographies over a large area while SRTM DEM (1arc second) with large global coverage provides elevation data with 30-meter resolution. GIS technique like assigning weights (weighted overlay method) on different parameters of the landslide can be helpful to policy makers and disaster management departments to overwhelm the serious situation during the hazard

Landslide Susceptibility Zone using Frequency Ratio Model, Remote Sensing &GIS –A Case Study of Western Ghats, India (Part of Kodaikanal Taluk)

Landslide is the most common natural hazards in hilly terrains due to factors like gravity, weathering, deforestation, earthquake, heavy precipitation etc results heavy loss causing widespread damage to property and infrastructure, besides loss of human lives almost every year. The aim of the study is to assess the Landslide susceptibility zones in the part of the area of Kodaikanal taluk, Dindigul District, Tamil Nadu using Remote sensing and GIS. In this Paper frequency ratio based on statistical methods is evaluated by interpretating the observed landslides and their controlling factors .The Most accurate results of frequency ratio is analyzed by using elevation, slope angle, slope aspect, geology, land use, distance to road, distance to drainage maps and confirmed by using previous empirical landslide  zonation data’s. Keywords: Landslide, Remote Sensing, GIS, Frequency Ratio.