Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine,Algeria

This research work deals with the landslide susceptibility assessment using Analytic hierarchy process (AHP) and information value (IV) methods along a highway road section in Constantine region, NE Algeria. The landslide inventory map which has a total of 29 single landslide locations was created based on historical information, aerial photo interpretation, remote sensing images, and extensive field surveys. The different landslide influencing geoenvironmental factors considered for this study are lithology, slope gradient, slope aspect, distance from faults, land use, distance from streams, and geotechnical parameters. A thematic layer map is generated for every geoenvironmental factor using Geographic Information System (GIS); the lithological units and the distance from faults maps were extracted from the geological database of the region. The slope gradient, slope aspect, and distance from streams were calculated from the Digital Elevation Model (DEM). Contemporary land use map was derived from satellite images and field study. Concerning the geotechnical parameters maps, they were determined making use of the geotechnical data from laboratory tests. The analysis of the relationships between the landslide-related factors and the landslide events was then carried out in GIS environment. The AUC plot showed that the susceptibility maps had a success rate of 77 and 66% for IV and AHP models, respectively. For that purpose, the IV model is better in predicting the occurrence of landslides than AHP one. Therefore, the information value method could be used as a landslide susceptibility mapping zonation method along other sections of the A1 highway.     

Analytic hierarchy process applied to landslide susceptibility mapping of the North Branch of Argentino Lake,Argentina

Natural Hazards - In the present study, we achieved the susceptibility mapping to slope instability processes by the implementation of Analytic Hierarchy Process and Weighted Linear Combination...     

Application of analytical hierarchy process and least-squares method for landslide susceptibility assessment along the Zhong-Wu natural gas pipeline, China

As one of the major problems of geo-engineering, landslides often influence the safety of linear engineering projects that cross mountainous areas. Therefore, when selecting suitable routes for such projects, it is important to assess their susceptibility to landslides. In this paper, we used a natural gas pipeline in the northeast of the Yunnan-Guizhou Plateau of China as a case study to analyze landslide susceptibility. Based on engineering geological analogy, the analytical hierarchy process, and the least-squares method, a regional landslide susceptibility assessment model was developed and was programmed using GIS ArcEngine components under the Visual Studio.NET environment. The landslide susceptibility along the Zhong-Wu natural gas pipeline from Zhongxian County to Wuhan was assessed based on this model and classified into five levels: very safe, safe, moderate, susceptible, and very susceptible. The high accuracy and prediction capability of the model were confirmed by comparing the model results with past landslide data and performing a prediction test. The results indicated that the assessment model used in this study is reliable and can be used for landslide susceptibility assessment and route selection in other areas.     

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.     

National-scale assessment of landslide susceptibility to rank the vulnerability to failure of rock-cut slopes along expressways in Korea

The objective of this study is to perform a preliminary national-scale assessment of the landslide susceptibility of rock-cut slopes along expressways in Korea. A geographic information system (GIS) database was compiled based on data from topographical and geological maps, and rock-cut slope data, including the locations of past landslides. Seven factors (i.e., slope height, slope length, slope gradient, upper slope gradient, lithology, distance from nearest fault, and dip direction of slope) were extracted from the GIS database to assess the relationship between each factor and landslide events. Weight of evidence (WOE), analytic hierarchy process (AHP), and fuzzy logic methods, as well as hybrid methods, were used to establish the rating of classes for each factor, weightings for the factors, and to combine multiple factor layers into landslide-susceptibility maps. A comparison of the results obtained using several different methods, based on the area under curve technique, revealed that the WOE method showed the highest accuracy of 74%. The annual cost of traffic congestion resulting from slope failures was evaluated to identify those rock-cut slopes where detailed investigations and landslide warning systems are required.     

GIS-based landslide susceptibility zonation and comparative analysis using analytical hierarchy process and conventional weighting-based multivariate statistical methods in the Lachung River Basin,North Sikkim

Natural Hazards - The timely and accurate assessment of casualties is the key for and basis of emergency rescue work after an earthquake. In this paper, through exponential fitting of historical...     

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya,India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps(LSMs) using computer-based advanced machine learning techniques and compare the performance of the models.To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model(CNN) and three popular machine learning techniques, i.e., random forest model(RF), artificial neural network model(ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and nonlandslide points. A ratio of 70:30 was considered for the selection of both training and validation points.Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve(AUC) of the receiver operating characteristics(ROC) curve and statistical methods like root mean square error(RMSE) and mean absolute error(MAE). In both datasets(training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.     

Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran

The main goal of this study is to produce landslide susceptibility maps of a landslide-prone area (Haraz) in Iran by using both fuzzy logic and analytical hierarchy process (AHP) models. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 78 landslides were mapped from various sources. Then, the landslide inventory was randomly split into a training dataset 70?% (55 landslides) for training the models and the remaining 30?% (23 landslides) was used for validation purpose. Twelve data layers, as the landslide conditioning factors, are exploited to detect the most susceptible areas. These factors are slope degree, aspect, plan curvature, altitude, lithology, land use, distance from rivers, distance from roads, distance from faults, stream power index, slope length, and topographic wetness index. Subsequently, landslide susceptibility maps were produced using fuzzy logic and AHP models. For verification, receiver operating characteristics curve and area under the curve approaches were used. The verification results showed that the fuzzy logic model (89.7?%) performed better than AHP (81.1?%) model for the study area. The produced susceptibility maps can be used for general land use planning and hazard mitigation purpose.     

Soft computing and GIS for landslide susceptibility assessment in Tawaghat area, Kumaon Himalaya, India

This paper mainly presents a case study of landslide vulnerability zonation along Tawaghat-Mangti route corridor in Kumaon Himalaya, India. An attempt is made to predict landslide susceptibility using back-propagation neural network (BPNN) and propose a suitable model for that zone, which can be successfully implemented for the prevention of slides. Various landslide affecting parameters such as lithology, slope, aspect, structure, geotechnical properties, land use, landslide inventory, and distance from recorded epicenter are used to model the landslide susceptibility. The database on the above parameters derived from satellite imageries, topographic maps, and field work are integrated in the GIS to generate an information layer. Database of this information layer is used to train, test, and validate the BPNN model. A three-layered BPNN with an input layer, two hidden layers, and one output layer is found to be optimal. The developed model demonstrates a promising result, and the prediction accuracy has been found to be 80?% in the field.     

Effect of using various weighting methods in a process of landslide susceptibility assessment

Natural Hazards - This study discusses the evaluation of the effect of using different weighting approaches in the process of landslide susceptibility assessment. Weighting process is needed,...     

Spatial data for landslide susceptibility, hazard, and vulnerability assessment: An overview

The aim of this paper is to discuss a number of issues related to the use of spatial information for landslide susceptibility, hazard, and vulnerability assessment. The paper centers around the types of spatial data needed for each of these components, and the methods for obtaining them. A number of concepts are illustrated using an extensive spatial data set for the city of Tegucigalpa in Honduras. The paper intends to supplement the information given in the “Guidelines for Landslide Susceptibility, Hazard and Risk Zoning for Land Use Planning” by the Joint ISSMGE, ISRM and IAEG Technical Committee on Landslides and Engineered Slopes (JTC-1). The last few decades have shown a very fast development in the application of digital tools such as Geographic Information Systems, Digital Image Processing, Digital Photogrammetry and Global Positioning Systems. Landslide inventory databases are becoming available to more countries and several are now also available through the internet. A comprehensive landslide inventory is a must in order to be able to quantify both landslide hazard and risk. With respect to the environmental factors used in landslide hazard assessment, there is a tendency to utilize those data layers that are easily obtainable from Digital Elevation Models and satellite imagery, whereas less emphasis is on those data layers that require detailed field investigations. A review is given of the trends in collecting spatial information on environmental factors with a focus on Digital Elevation Models, geology and soils, geomorphology, land use and elements at risk.     

Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances

The current research presents a detailed landslide susceptibility mapping study by binary logistic regression, analytical hierarchy process, and statistical index models and an assessment of their performances. The study area covers the north of Tehran metropolitan, Iran. When conducting the study, in the first stage, a landslide inventory map with a total of 528 landslide locations was compiled from various sources such as aerial photographs, satellite images, and field surveys. Then, the landslide inventory was randomly split into a testing dataset 70 % (370 landslide locations) for training the models, and the remaining 30 % (158 landslides locations) was used for validation purpose. Twelve landslide conditioning factors such as slope degree, slope aspect, altitude, plan curvature, normalized difference vegetation index, land use, lithology, distance from rivers, distance from roads, distance from faults, stream power index, and slope-length were considered during the present study. Subsequently, landslide susceptibility maps were produced using binary logistic regression (BLR), analytical hierarchy process (AHP), and statistical index (SI) models in ArcGIS. The validation dataset, which was not used in the modeling process, was considered to validate the landslide susceptibility maps using the receiver operating characteristic curves and frequency ratio plot. The validation results showed that the area under the curve (AUC) for three mentioned models vary from 0.7570 to 0.8520 $ ({\text{AUC}}_{\text{AHP}} = 75.70\;\% ,\;{\text{AUC}}_{\text{SI}} = 80.37\;\% ,\;{\text{and}}\;{\text{AUC}}_{\text{BLR}} = 85.20\;\% ) $ ( AUC AHP = 75.70 % , AUC SI = 80.37 % , and AUC BLR = 85.20 % ) . Also, plot of the frequency ratio for the four landslide susceptibility classes of the three landslide susceptibility models was validated our results. Hence, it is concluded that the binary logistic regression model employed in this study showed reasonably good accuracy in predicting the landslide susceptibility of study area. Meanwhile, the results obtained in this study also showed that the statistical index model can be used as a simple tool in the assessment of landslide susceptibility when a sufficient number of data are obtained.     

Application and verification of a fractal approach to landslide susceptibility mapping

Landslide susceptibility mapping is essential for land-use activities and management decision making in hilly or mountainous regions. The existing approaches to landslide susceptibility zoning and mapping require many different types of data. In this study, we propose a fractal method to map landslide susceptibility using historical landslide inventories only. The spatial distribution of landslides is generally not uniform, but instead clustered at many different scales. In the method, we measure the degree of spatial clustering of existing landslides in a region using a box-counting method and apply the derived fractal clustering relation to produce a landslide susceptibility map by means of GIS-supported spatial analysis. The method is illustrated by two examples at different regional scales using the landslides inventory data from Zhejiang Province, China, where the landslides are mainly triggered by rainfall. In the illustrative examples, the landslides from the inventory are divided into two time periods: The landslides in the first period are used to produce a landslide susceptibility map, and those in the late period are taken as validation samples for examining the predictive capability of the landslide susceptibility maps. These examples demonstrate that the landslide susceptibility map created by the proposed technique is reliable.     

A comparison of slope units and grid cells as mapping units for landslide susceptibility assessment

Landslides lead to a great threat to human life and property safety. The delineation of landslide-prone areas achieved by landslide susceptibility assessment plays an important role in landslide management strategy. Selecting an appropriate mapping unit is vital for landslide susceptibility assessment. This paper compares the slope unit and grid cell as mapping unit for landslide susceptibility assessment. Grid cells can be easily obtained and their matrix format is convenient for calculation. A slope unit is considered as the watershed defined by ridge lines and valley lines based on hydrological theory and slope units are more associated with the actual geological environment. Using 70% landslide events as the training data and the remaining landslide events for verification, landslide susceptibility maps based on slope units and grid cells were obtained respectively using a modified information value model. ROC curve was utilized to evaluate the landslide susceptibility maps by calculating the training accuracy and predictive accuracy. The training accuracies of the grid cell-based susceptibility assessment result and slope unit-based susceptibility assessment result were 80.9 and 83.2%, and the prediction accuracies were 80.3 and 82.6%, respectively. Therefore, landslide susceptibility mapping based on slope units performed better than grid cell-based method.     

Application of frequency ratio,statistical index,and weights-of-evidence models and their comparison in landslide susceptibility mapping in Central Nepal Himalaya

The Mugling–Narayanghat road section falls within the Lesser Himalaya and Siwalik zones of Central Nepal Himalaya and is highly deformed by the presence of numerous faults and folds. Over the years, this road section and its surrounding area have experienced repeated landslide activities. For that reason, landslide susceptibility zonation is essential for roadside slope disaster management and for planning further development activities. The main goal of this study was to investigate the application of the frequency ratio (FR), statistical index (SI), and weights-of-evidence (WoE) approaches for landslide susceptibility mapping of this road section and its surrounding area. For this purpose, the input layers of the landslide conditioning factors were prepared in the first stage. A landslide inventory map was prepared using earlier reports, aerial photographs interpretation, and multiple field surveys. A total of 438 landslide locations were detected. Out these, 295 (67 %) landslides were randomly selected as training data for the modeling using FR, SI, and WoE models and the remaining 143 (33 %) were used for the validation purposes. The landslide conditioning factors considered for the study area are slope gradient, slope aspect, plan curvature, altitude, stream power index, topographic wetness index, lithology, land use, distance from faults, distance from rivers, and distance from highway. The results were validated using area under the curve (AUC) analysis. From the analysis, it is seen that the FR model with a success rate of 76.8 % and predictive accuracy of 75.4 % performs better than WoE (success rate, 75.6 %; predictive accuracy, 74.9 %) and SI (success rate, 75.5 %; predictive accuracy, 74.6 %) models. Overall, all the models showed almost similar results. The resultant susceptibility maps can be useful for general land use planning.