Resulting Landslide Susceptibility Maps Research Articles

Small Landslide Susceptibility and Hazard Assessment Based on Airborne Lidar Data

AbstractLandslides are natural disasters that cause environmental and infrastructure damage worldwide. To prevent future risk posed by such events, effective methods to detect and map their hazards are needed. Traditional landslide susceptibility mapping techniques, based on field inspection, aerial photograph interpretation, and contour map analysis are often subjective, tedious, difficult to implement, and may not have the spatial resolution and temporal frequency necessary to map small slides, which is the focus of this investigation.We present a methodology that is based on a Support Vector Machine (SVM) that utilizes a lidar-derived Digital Elevation Model (DEM) to quantify and map the topographic signatures of landslides. The algorithm employs several geomorphological features to calibrate the model and delineate between landslide and stable terrain. To evaluate the performance of the proposed algorithm, a road corridor in Zanesville, Ohio, was used for testing. The resulting landslide susceptibility map was validated to correctly identify 67 of the 80 mapped landslides in the independently compiled landslide inventory map of the area. These results suggest that the proposed landslide surface feature extraction method and airborne lidar data can be used as efficient tools for small landslide susceptibility and hazard mapping.

Spatial landslide hazard assessment along the Jelapang Corridor of the North-South Expressway in Malaysia using high resolution airborne LiDAR data

Mapping landslide-prone regions are crucial in natural hazard management and urban development activities in hilly and tropical regions. This research aimed to delineate a spatial prediction of landslide hazard areas along the Jelapang Corridor of the North-South Expressway in Malaysia by using two statistical models, namely, logistic regression (LR) and evidential belief function (EBF). Landslides result in high economic and social loses in Malaysia, particularly to highway concessionaries such as PLUS Expressways Berhad. LR and EBF determine the correlation between conditioning factors and landslide occurrence. EBF can also be applied in bivariate statistical analysis. Thus, EBF can be used to assess the effect of each class of conditioning factors on landslide occurrence. A landslide inventory map with 26 landslide sites was recorded using field measurements. Subsequently, the landslide inventory was randomly divided into two data sets. Approximately 70 % of the data were used for training the models, and 30 % were used for validating the results. Eight landslide conditioning factors were prepared for landslide susceptibility analysis: altitude, slope, aspect, curvature, stream power index, topographic wetness index, terrain roughness index, and distance from river. The landslide probability index was derived from both methods and subsequently classified into five susceptible classes by using the quantile method. The resultant landslide susceptibility maps were evaluated using the area under the curve technique. Results revealed the proficiency of the LR method in landslide susceptibility mapping. The achieved success and prediction rates for LR were 90 and 88 %, respectively. However, EBF was not successful in providing reasonable accurate results. The acquired success and prediction rates for EBF were 53 and 50 %, respectively. Hence, the LR technique can be utilized in landslide hazard studies for land use management and planning.

Landslide susceptibility assessment at Wadi Jawrah Basin, Jizan region, Saudi Arabia using two bivariate models in GIS

This paper presents an evaluation of two bivariate statistical approaches, frequency ratio (FR) and weights-of-evidence (WoE) for landslide susceptibility mapping (LSM) at south-western part of Saudi Arabia, Jizan region. Landslide locations were identified and mapped from interpretation of high resolution satellite images (GeoEye 0.5 m and QuikeBird 0.6m), topographic maps (scale of 1:10,000), historical records, and extensive field surveys. A total of 106 landslide locations were mapped using Arc-GIS software and divided into two groups, 75% and 25% of landslide locations were used for training and validation of models, respectively. Eleven landslide conditioning factors such as elevation, slope, curvature, aspect, lithology, topographic wetness index (TWI), normalized difference vegetation index (NDVI), proximity to lineament, roads and rivers were considered in this evaluation. The effects of these factors on landslide occurrence were assessed using aforementioned bivariate statistical approaches. For validation, the models results were compared with landslide locations which were not used during the models building. Subsequently, the receiver operating characteristic (ROC) curves were established and area under the curves (AUC) was calculated for the landslide susceptibility maps using the success (training data) and prediction (validation data) rates. The results showed that the area under the curve for success rates are 0.861 (86.1%) and 0.839 (83.9%) and for prediction rates are 0.796 (79.6%) and 0.791 (79.1%), respectively for frequency ratio and weight-of-evidence models. The resulting landslide susceptibility maps showed five classes of susceptibility such as very high, high, moderate, low, and very low. The percentage of existing training and validating landslides data in high and very high zones of the susceptibility maps were calculated to be 90.02% and 76.03% for frequency ratio model and 88.33% and 79.3% for weight-of-evidence model, respectively. The results revealed that the frequency ratio and weights-of-evidence models produced reasonable accuracy. The resultant maps would be useful and can also help planners for general choosing favorable locations for development schemes, such as infrastructural, buildings, road constructions, and environmental protection.

Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea

Landslides susceptibility maps were constructed in Seorak mountain area, Korea, using an integration of frequency ratio and adaptive neuro-fuzzy inference system (ANFIS) in geographical information system (GIS) environment. Landslide occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models, respectively. Topography, geology, soil, and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for landslide susceptibility mapping in the study area. Two landslide susceptibility maps were prepared using the different MFs. The frequency ratio model was also applied to the landslide susceptibility mapping for comparing with the probabilistic ANFIS model. Finally, the resulting landslide susceptibility maps were validated using the landslide locations which were not used for training the ANFIS. The validation results showed 75.57 % accuracy using the generalized bell-shaped MF model, 74.94 % accuracy using the Sigmoidal 2 MF model and 73.07 % accuracy using frequency ratio model. These accuracy results show that an ANFIS can be an effective tool in landslide susceptibility mapping.

A GIS-based extended fuzzy multi-criteria evaluation for landslide susceptibility mapping

Landslide susceptibility mapping (LSM) is making increasing use of GIS-based spatial analysis in combination with multi-criteria evaluation (MCE) methods. We have developed a new multi-criteria decision analysis (MCDA) method for LSM and applied it to the Izeh River basin in south-western Iran. Our method is based on fuzzy membership functions (FMFs) derived from GIS analysis. It makes use of nine causal landslide factors identified by local landslide experts. Fuzzy set theory was first integrated with an analytical hierarchy process (AHP) in order to use pairwise comparisons to compare LSM criteria for ranking purposes. FMFs were then applied in order to determine the criteria weights to be used in the development of a landslide susceptibility map. Finally, a landslide inventory database was used to validate the LSM map by comparing it with known landslides within the study area. Results indicated that the integration of fuzzy set theory with AHP produced significantly improved accuracies and a high level of reliability in the resulting landslide susceptibility map. Approximately 53% of known landslides within our study area fell within zones classified as having “very high susceptibility”, with the further 31% falling into zones classified as having “high susceptibility”.

Landslide susceptibility mapping along Bhalubang — Shiwapur area of mid-Western Nepal using frequency ratio and conditional probability models

Roads constructed in fragile Siwaliks are prone to large number of instabilities. Bhalubang-Shiwapur section of Mahendra Highway lying in Western Nepal is one of them. To understand the landslide causative factor and to predict future occurrence of the landslides, landslide susceptibility mapping (LSM) of this region was carried out using frequency ratio (FR) and weights-of-evidence (W of E) models. These models are easy to apply and give good results. For this, landslide inventory map of the area was prepared based on the aerial photo interpretation, from previously published/unpublished reposts, and detailed field survey using GPS. About 332 landslides were identified and mapped, among which 226 (70%) were randomly selected for model training and the remaining 106 (30%) were used for validation purpose. A spatial database was constructed from topographic, geological, and land cover maps. The reclassified maps based on the weight values of frequency ratio and weights-of-evidence were applied to get final susceptibility maps. The resultant landslide susceptibility maps were verified and compared with the training data, as well as with the validation data. From the analysis, it is seen that both the models were equally capable of predicting landslide susceptibility of the region (W of E model (success rate = 83.39%, prediction rate = 79.59%); FR model (success rate = 83.31%, prediction rate = 78.58%)). In addition, it was observed that the distance from highway and lithology, followed by distance from drainage, slope curvature, and slope gradient played major role in the formation of landsides. The landslide susceptibility maps thus produced can serve as basic tools for planners and engineers to carry out further development works in this landslide prone area.

Spatial prediction of landslide susceptibility using a decision tree approach: a case study of the Pyeongchang area, Korea

A decision tree approach was applied and validated for analysis of landslide susceptibility using a geographic information system (GIS). The study area was the Pyeongchang area in Gangwon Province, Korea, where many landslides occurred in 2006 and where the 2018 Winter Olympics are to be held. Spatial data, such as landslides, topography, and geology, were detected, collected, and compiled in a database using remote sensing and GIS. The 3994 recorded landslide locations were randomly split 50/50 for training and validation of the models. A decision tree model, which is a type of data-mining classification model, was applied and decision trees were constructed using the chi-squared (χ2) automatic interaction detector (CHAID) and the quick, unbiased, and efficient statistical tree (QUEST) algorithms. Also, as a reference, a frequency-ratio model was applied using the same database. The relationships between the detected landslide locations and their factors were identified and quantified by frequency-ratio and decision tree models. The relationships were used as factor ratings in the overlay analysis to create landslide susceptibility indices and maps. Then, the resulting landslide-susceptibility maps were validated using area-under-the-curve (AUC) analysis with the landslide area data that had not been used for training the model. The decision tree models using the CHAID and QUEST algorithms had accuracies of 81.56% and 80.91%, respectively, which were somewhat better than the results for the frequency-ratio model (80.15%). These results indicate that decision tree models using the CHAID and QUEST algorithms can be useful for landslide susceptibility analysis.

Landslide susceptibility mapping by comparing the WLC and WofE multi-criteria methods in the West Crete Island, Greece

In the current research, the weighted linear combination (WLC) and the weights of evidence (WofE) methods were applied for landslide susceptibility zonation mapping in the Chania Prefecture of Crete Island, Greece. Several thematic maps representing various landslide conditioning factors, such as geology formations, faults proximity, altitude, slope gradient, aspect, curvature, rivers proximity, precipitation, roads proximity and land use types have been generated in a GIS environment. Three landslide susceptibility maps were created, one for each method and a combined one by applying the logistic regression approach to the WLC and WofE susceptibility maps. The maps were evaluated and validated using the efficiency rate curve, the receiver-operator curve and the spatially agreed areas methods. The resulting landslide susceptibility maps have uncertainties introduced due to the subjective knowledge of experts in the case of WLC method and to the quality of the recorded landslides sample in the case of the WofE method. Nevertheless, the performance of the three approaches was found to be almost equal with all methods to produce quite satisfactory results. Besides the comparison of the applied methods, the current study contributes to the risk management of the area, providing the first landslide susceptibility maps for Chania Prefecture.

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote-sensing methods have the spatial and temporal resolution required to map hazard increases. Here, a dynamic physically-based slope stability model that requires soil moisture is applied using remote-sensing products from multiple Earth observing platforms. The resulting landslide susceptibility maps using the advanced microwave scanning radiometer (AMSR-E) surface soil moisture are compared to those created using variable infiltration capacity (VIC-3L) modeled soil moisture at Cleveland Corral landslide area in California, US. Despite snow cover influences on AMSR-E surface soil moisture estimates, a good relationship between the downscaled AMSR-E's surface soil moisture and the VIC-3L modeled soil moisture is evident. The AMSR-E soil moisture mean (0.17 cm 3/cm 3) and standard deviation (0.02 cm 3/cm 3) are very close to the mean (0.21 cm 3/cm 3) and standard deviation (0.09 cm 3/cm 3) estimated by VIC-3L model. Qualitative results show that the location and extent of landslide prone regions are quite similar. Under the maximum saturation scenario, 0.42% and 0.49% of the study area were highly susceptible using AMSR-E and VIC-3L model soil moisture, respectively.

A static and dynamic factors-coupled forecasting model of regional rainfall-induced landslides: A case study of Shenzhen

Most rainfall-induced landslide forecasting models focus on the relation between landslides and rainfall, which is one of the dynamic factors, and seldom consider the stacitc factors, such as geological and geograpical factors. Landslide susceptibility, however, is determinded by both static and dynamic factors. This article proposes a static and dynamic factors-coupled forecasting model (SDFCFM) of regional rainfall-induced landslides, which quantitatively considers both the static and dynamic factors that affect landslides. The generalized additive model (GAM) is applied to coupling both factors to get the landslide susceptibility. In the case study, SDFCFM is applied to forecast the landslide occurrences in Shenzhen during a rainfall process in 2008. Compared with the rainfall logistic regression model, the resulting landslide susceptibility map illustrates that SDFCFM can reduce the forecast redundancy and improve the hit ratio. It is both applicable and practical. The application of SDFCFM in landslide warning and prevention system will improve its efficiency and also cut down the cost of human and matreial resources.

An Integrated Approach for Landslide Susceptibility Mapping Using Remote Sensing and GIS

A methodology for landslide susceptibility mapping using an integrated remote sensing and GIS approach is presented. A part of the Darjeeling Himalaya was selected for the model execution. IRS satellite data, topographic maps, field data, and other informative maps were used as inputs to the study. Important terrain factors, contributing to landslide occurrences in the region, were identified and corresponding thematic data layers were generated. These data layers represent the geological, topographical, and hydrological conditions of the terrain. A numerical rating scheme for the factors was developed for spatial data analysis in a GIS. The resulting landslide susceptibility map delineates the area into different zones of four relative susceptibility classes: high, moderate, low, and very low. The susceptibility map was validated by correlating the landslide frequencies of different classes. This has shown a close agreement with the existing field instability condition. The effectiveness of the map was also confirmed by the high statistically significant value of a chi-square test.

Assessment of landslide susceptibility for a landslide-prone area (north of Yenice, NW Turkey) by fuzzy approach

Regional landslide susceptibility assessments pose complex problems. To solve these problems, numerous approaches, such as statistical analysis, geotechnical engineering approach, geomorphologic approach and fuzzy logic, have been employed. However, all the available methods for regional landslide susceptibility assessments have some uncertainties due to a lack of knowledge and variability. Minimizing these uncertainties provides realistic approaches. Use of the fuzzy logic approach to produce a landslide susceptibility map of a landslide-prone area in NW Turkey is the main purpose of the present study. For this purpose, the study includes five main stages, these being the preparation of a landslide inventory of the study area, the application of factor analysis, the extraction of fuzzy if-then rules, the use of a geographical information system, and the control of the reliability of the resulting landslide susceptibility map. Slope angle, slope aspect, land use, weathering depth, water conditions and topographical elevation were considered as landslide conditioning factors for the study area. A total of 23 if-then rules was extracted from the field data. Employing these rules, fuzzified index maps representing each parameter were obtained. Finally, combining these maps, the landslide susceptibility map of the area was prepared. When compared with the landslide susceptibility map, the landslides identified in the area were found to be located in the very high- and high-susceptibility zones. As far as the performance of the fuzzy approach for processing is concerned, the images appear to be quite satisfactory, the zones determined on the map being zones of relative susceptibility.