Regional-scale landslide susceptibility mapping using the weights of evidence method: an example applied to linear infrastructure

2010 ◽  
Vol 47 (8) ◽  
pp. 905-927 ◽  
Author(s):  
P. E. Quinn ◽  
D. J. Hutchinson ◽  
M. S. Diederichs ◽  
R. K. Rowe
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Eastern Canada ◽  
Soil Thickness ◽  
Total Length ◽  
Susceptibility Model ◽  
Total Inventory ◽  
Bivariate Statistical Method

Large landslides are common in the gently sloping clay plains of the Saint Lawrence Lowlands of eastern Canada. These tend to occur along rivers carved into the marine soils deposited in the former Champlain Sea, which occupied the area roughly 10 000 years ago. This paper presents a landslide susceptibility model, developed at the regional scale using a bivariate statistical method: the weights of evidence method. The analysis considers the association of existing large landslides in a portion of the study area with key terrain features, such as ground elevation, flow accumulation in adjacent streams, soil type, soil thickness, and land use. The resulting model identifies three different levels of susceptibility: low, low to moderate, and moderate to high. These descriptors are related statistically to the probability of encountering existing large landslides within 500 m, 1 or 2 km, respectively. The model is tested along primary railway corridors and isolates 8% of the total length for further consideration of landslide hazard. Reconnaissance level air photo survey results further reduce the length of corridor with elevated susceptibility to 2% of the total length, thus focusing the application of additional resources to a very small proportion of the total inventory.

scholarly journals GIS-Based Weights-of-Evidence Model for Landslide Susceptibility Mapping and Assessment in the Hillslopes of Bujumbura, Burundi

2021 ◽  
Author(s):  
Désiré Kubwimana ◽  
Lahsen Ait Brahim ◽  
Abdellah Abdelouafi
Keyword(s):  
Landslide Susceptibility ◽  
Relevant Literature ◽  
Regional Scale ◽  
Slope Angle ◽  
Weights Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Fault Density ◽  
Evidence Model

Abstract The aim of this research is the modelling of landslide susceptibility in the hillslopes of Bujumbura using the Weights-of-Evidence model, a probabilistic data modelling approach relevant for predicting future landslides at a regional scale. Initially, characteristics and spatial mapping of different landslides type were identified (fall, flow, slide, complex) by thorough interpretation of high-resolution remote sensing data (mountainous areas with difficult access) and intensive fieldwork. Subsequently, the main landslides controlling factors were selected (lithology, fault density, land use, drainage density, slope aspect, curvature, slope angle, and elevation) using in-depth field knowledge and relevant literature. A landslide inventory map with a total of 569 landslide sites was constructed using the data from various sources. Out of those 569 landslide sites, 285 (50.1%) of the data taken before the 2000s was used for training and the remaining 284 (49.9%) sites (post-2000 events) were used for the accuracy assessment purpose. Thereafter, a prediction map of future landslides was generated with an accuracy of 73.7%. The main geo-environmental landslides factors retained are the high density of drainage networks, the lithology often made with weathered gneiss, the high fault density, the steep topography and the convex slope curvature. The landslide susceptibility map validated was reclassified into very high, high, moderate, low and very low zones. The established susceptibility map will allow with the interaction of the real terrain to locate roads, dwellings, urban extension areas, dams located in high landslides risk zones. These infrastructures will require intervention to address their vulnerability with new facilities, slope stabilization, creation of bypass roads, etc. The susceptibility map produced will be a powerful decision-making tool for drawing up appropriate development plans. Such an approach will make it possible to mitigate the socio-economic impacts due to slope instabilities.

Communality from equal weight for GIS-based landslide susceptibility mapping

2020 ◽  
Author(s):  
Sandip Som ◽  
Saibal Ghosh ◽  
Soumitra Dasgupta ◽  
Thrideep Kumar ◽  
J. N. Hindayar ◽  
...  
Keyword(s):  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Regional Scale ◽  
Model Development ◽  
Model Performance ◽  
Roc Curves ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Equal Weight ◽  
Landslide Susceptibility Index

Abstract Modeling landslide susceptibility is one of the important aspects of land use planning and risk management. Several modeling methods are available based either on highly specialized knowledge on causative attributes or on good landslide inventory data to use as training and testing attribute on model development. Understandably, these two criteria are rarely available for local land regulators. This paper presents a new model methodology, which requires minimum knowledge of causative attributes and does not depend on landslide inventory. As landslide causes due to the combined effect of causative attributes, this model utilizes communality (common variance) of the attributes, extracted by exploratory factor analysis and used for calculation of landslide susceptibility index. The model can understand the inter-relationship of different geo-environmental attributes responsible for landslide along with identification and prioritization of attributes on model performance to delineate non-performing attributes. Finally, the model performance is compared with the well established AHP method (knowledge driven) and FRM method (data driven) by cut-off independent ROC curves along with cost-effectiveness. The model shows it’s performance almost at par with the established models, involving minimum modeling expertise. The findings and results of the present work will be helpful for the town planners and engineers on a regional scale for generalized planning and assessment.

scholarly journals Landslide susceptibility assessment in complex geological settings: sensitivity to geological information and insights on its parameterization

Landslides ◽  
2020 ◽  
Vol 17 (10) ◽  
pp. 2443-2453 ◽  
Author(s):  
Samuele Segoni ◽  
Giulio Pappafico ◽  
Tania Luti ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Assessment ◽  
Geological Maps ◽  
Geological Information ◽  
Susceptibility Model ◽  
Validation Procedure ◽  
Geological Map

AbstractThe literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition.

scholarly journals Assessing the importance of feature selection in Landslide Susceptibility for Belluno province (Veneto Region, NE Italy)

2021 ◽  
Author(s):  
Sansar Raj Meena ◽  
Silvia Puliero ◽  
Kushanav Bhuyan ◽  
Mario Floris ◽  
Filippo Catani
Keyword(s):  
Landslide Susceptibility ◽  
Regional Scale ◽  
Belief Function ◽  
Landslide Susceptibility Mapping ◽  
Landslide Risk ◽  
Veneto Region ◽  
Conditioning Factors ◽  
Topographic Features ◽  
Ne Italy ◽  
Trial And Error Method

Abstract. In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno province (Veneto Region, NE Italy). The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and ML algorithms. By the trial-and-error method, we eliminated the least "important" features by using a common threshold. Conclusively, we found that removing the least "important" features does not impact the overall accuracy of the LSM for all three models. Based on the results of our study, the most commonly available features, for example, the topographic features, contributes to comparable results after removing the least "important" ones. This confirms that the requirement for the important factor maps can be assessed based on the physiography of the region. Based on the analysis of the three models, it was observed that most commonly available feature data can be useful for carrying out LSM at regional scale, eliminating the least available ones in most of the use cases due to data scarcity. Identifying LSMs at regional scale has implications for understanding landslide phenomena in the region and post-event relief measures, planning disaster risk reduction, mitigation, and evaluating potentially affected areas.

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