Landslide Inventory Research Articles

Landslide Inventory in the Downstream of the Niulanjiang River with ALOS PALSAR and Sentinel-1 Datasets

Landslide inventory and deformation monitoring is an essential task for human life and property security during the exploitation process of hydroelectric power resources. Synthetic Aperture Radar Interferometry (InSAR) is recognized as an effective tool for ground displacement monitoring with the advantages of wide coverage and high accuracy. In this study, we mapped the unstable slopes in the downstream of the Niulanjiang River with 22 ALOS PALSAR SAR images acquired from 2007 to 2011, and 90 Sentinel-1 SAR images from 2015 to 2019. A total of 94 active slopes are identified using a displacement map from the two datasets based on Small BAseline Subset (SBAS) InSAR analysis. By comparing the results from ALOS PALSAR and Sentinel-1 data stacks, we find that the number of active slopes increased dramatically. Several impact factors, e.g., earthquake, concentrated rainfall, and construction of hydropower stations, are discussed through time series analysis of typical landslides. Furthermore, nonlinear displacement of natural unstable slopes are found to be correlated with rainfall. A climate-driven model is used to qualify the relationship between rainfall and landslide displacement. Our results can provide valuable information for landslide detection and prevention.

A Novel Intelligent Method Based on the Gaussian Heatmap Sampling Technique and Convolutional Neural Network for Landslide Susceptibility Mapping

Landslide susceptibility mapping (LSM) is significant for disaster prevention and mitigation, land use management, and as a reference for decision-making. Convolutional neural networks (CNNs) in deep learning have been successfully applied to LSM studies and have been shown to improve the accuracy of LSM. Although optimizing the quality of negative samples at the input step of a deep learning model can improve the accuracy of the model, the risk of model overfitting may increase. In this study, an LSM method based on the Gaussian heatmap sampling technique and a CNN was developed from the perspective of LSM dataset sampling. A Gaussian heatmap sampling technique was used to enrich the variety of landslide inventory at the input step of the deep learning model to improve the accuracy of the LSM results. This sampling technique involved the construction of a landslide susceptibility Gaussian heatmap neural network model, LSGH-Net, by combining a CNN. A series of optimization strategies such as attention mechanism, dropout, etc., were applied to improve the model structure and training process. The results demonstrated that the proposed approach outperformed the benchmark CNN-based algorithm in terms of metrics (Accuracy = 95.30%, F1 score = 95.13%, and Sensitivity = 91.79%). The Gaussian heatmap sampling technique effectively improved the accuracy of frequency histograms of the landslide susceptibility index, which provided finer-grained mapping details and more reasonable landslide density. By analyzing Gaussian heatmap at different scales, the approach proposed in this paper is an important reference for different regions and other disaster susceptibility studies as well.

Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology

This study reported the causes of prediction uncertainty in landslide susceptibility, based on machine learning. The landslide inventory and conditioning factors were first prepared. Thereafter, factor analysis, including the effectiveness, multicollinearity, and importance was performed for the subsequent susceptibility modeling. Finally, the predicted capacity was explored in terms of model performance and map performance. Investigation indicates that landslides are focused on the central and southeastern part of the study area, displaying the characteristics of small-scale and shallow sliding. The difference in the ML algorithm, kernel functions, the selection of base-classifier, the evaluation indicators of performance, and even the factor zonation results in difficulties in determining the optimal ML model. Besides, the DT-Boosting keeps the high AUC value, 0.981 and 0.862 respectively in the training and test phases, while the RF holds a highly robust in terms of fluctuation characteristics. Indeed, the susceptibility map generated by RBF-ANN can better capture the difference in landslide susceptibility, with the D-value of 7.9. This study provides an in-depth understanding of the uncertainty of the predicted capacity in ML-based landslide susceptibility.

Large-scale landslide inventory and their mobility in Lvliang City, Shanxi Province, China

Landslides are one of the most destructive geological disasters that threaten people's lives and property. With the high-resolution satellite images from Google Earth and the method of visual interpretation, we develop a large-scale landslide inventory in Lvliang City in the east of the Loess Plateau. The inventory includes a total of 12,110 landslides, and the area of each individual landslide ranges from 0.03 to 1.35 ​km2. The landslide area ratio (LAR) is used to measure the influence of several geological factors on landslides, including stratigraphic chronology, elevation and slope. From Paleozoic to Cenozoic, LAR generally shows an increasing trend. With the increase of elevation, LAR initially increases and then decreases, reaching the peak value in the elevation range of 1000–1100 ​m. With the increase of slope angle, LAR also increases first and subsequently decreases, and reaches the peak value in the range of 15–20°. In addition, we analyze the landslide mobility level (H/L) of 12,110 landslides in the study area, and observe a linear relationship between landslide height and sliding distance. Landslides with larger areas exhibit high mobility, manifested as the fact that H/L decreases with the increase of the landslide area. Meanwhile, both slope angle and aspect exert obvious effects on the mobility of landslides.

Landslide Susceptibility Prediction Based on Frequency Ratio Method and C5.0 Decision Tree Model

This paper aims to propose an efficient landslide susceptibility prediction (LSP) model based on the frequency ratio method and C5.0 Decision Tree (C5.0 DT) model. Taking Ruijin City as the study area, local landslide inventory and 12 environmental factors are collected. Then the nonlinear correlations between landslide inventory and environmental factors are established by frequency ratio (FR) method. Thirdly, the FR values of these environmental factors are taken as the input variables of the C5.0 DT/SVM models; landslide samples and non-landslide samples are set as the output variables with values of 1 and 0, respectively. The mathematical relationship between input variables and output variables is established by C5.0 DT/SVM models. Finally, the performance of LSP of both models is evaluated by the Area Under Receiver Operation Characteristic Curve (AUC). Results show that: 1) The landslide susceptibility mapping (LSM) of the C5.0 DT and the SVM models are similar on the whole, high and very high susceptibility levels are mainly distributed in the north and in the edge of the study area. 2) The AUC values of C5.0 DT and SVM are 0.886 and 0.819, respectively. Both models have good LSP accuracy, however, the overall LSP accuracy of the C5.0 DT model is better than that of SVM. 3) It is significant and reliable to carry out LSP based on frequency ratio method and C5.0 DT model.

Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories

Landslide susceptibility prediction using an incremental learning Bayesian Network model considering the continuously updated landslide inventories

LANDSLIDE EVOLUTION PATTERN REVEALED BY MULTI-TEMPORAL DSMS OBTAINED FROM HISTORICAL AERIAL IMAGES

Abstract. Landslides are a widespread natural hazard that cause damages to people and to the built up environment. Accurate knowledge of landslide distribution is crucial to develop planning strategies, prevention and resilient communities worldwide. One of the most diffuse way of reporting landslides distribution in a territory is by preparing landslide inventory maps. Such a task is mostly accomplished by expert photo-interpretation of historical aerial photographs, which are an invaluable source of information because they portray the landscape when the anthropic pressure was lower than the present day, providing an observation of the landscape closer to the natural conditions. Despite such a common use of aerial photographs, they are poorly exploited to obtain quantitative measures to support landslide mapping activities. In this paper we present a comparison of two photogrammetric approaches to measure elevation changes in a 50-years period for an area densely affected by landslides in Southern Italy. The obtained results allowed to revisit the original expert mapping proving that such a method is a useful tool to support geomorphological mapping and to improve the overall accuracy of landslide inventories.

Landslide Risk Analysis Using Machine Learning Principles: A Case Study of Bukit Antrabangsa Landslide Incidence

A quantitative analysis of landslides was carried out in this research to ascertain the risk that will be incurred on housing development built along hillside slopes. The study uses results from the most recent landslides analysis of GIS data and soft computing techniques to describe a concept for determining risk in a housing development situated on the hillside. The concepts presented by this study are timely, which means that computations define the time in which the housing development becomes vulnerable to landslides. The general idea enclosed in this research was to merge recent landslides analysis with real-life situations. The study location for this research was an already developed area situated along the hillside slopes of Bukit Antrabangsa, Malaysia. The approach includes predicting landslides using a novel machine-learning algorithm ensemble, the random subspace technique. The prediction process relates to the probability of occurrence of the slides responsible for the vulnerability of the housing development. A landslides prediction technique that employs GIS data to develop a geospatial database and machine learning algorithms for predicting future occurrences was used to produce the landslides inventory. The study area’s landslide inventory was then used as reference locations to predict landslide occurrence under the influence of ten landslide predisposing factors. Prediction results were evaluated for accuracy using the ROC (receiver operator characteristics) and calculated the AUC (area under the curve). Other parameters used to decide the quality of the models include the RSME (root-mean-square error), the MAE (mean absolute error), and the F-measure. The results obtained from the statistical analysis of the model show that the model has high predictive and success rates. The soft computing results now gave ways to obtain a timely probability of occurrence of the slides using a deterministic approach. The analysis was concluded by providing a conceptual equation to determine the timely probability of the slides using the available hazard information.

Disaster Risk Management Of Landslides In Peninsular Malaysia

Landslide is one of the major disasters that occur in Malaysia. Despite various initiatives undertaken by the government to reduce their occurrences, landslide incidents continue to occur, resulting in loss of life and property. The development process coupled with climate change increases the risk of landslides. This manuscript briefly looks at the causal factors of landslides and identifies requirements for landslide risk management to identify options to strengthen landslide risk management in Peninsular Malaysia. Risk management encompassing identification of hazards, vulnerabilities and exposure of elements can prevent landslides from becoming a disaster. The study conducted, noted that the present approach in landslide disaster management has not fully incorporated the landslide disaster risk management approach. Key aspects such as legislation, institutions, zoning of disaster risk areas, risk transfer mechanisms and financial allocations require serious consideration. The determination of the legal basis to incorporate risk management measures, which will set out the needed mandates, jurisdiction and responsibility is one of the important steps. It will help ensure that decision making processes are risk-based, and strengthen the processes in preparing disaster risk maps, comprehensive databases using landslide inventory and register. It would also help in the preparation of early warning and emergency response systems as well as bolster community involvement, awareness, and public education. These will require a holistic approach in landslide disaster risk management. It will support the effort undertaken by government and stakeholders to incorporate the four priority areas of the Sendai Framework for Disaster Risk Reduction 2015-2030 which can effectively help reduce the risk of landslides.

Lateral sediment connectivity of landslides occurred under a heavy rainstorm and its influence on sediment yield of slope-channel cascade on the Loess Plateau

The rate at which sediment enters the channel network in the landscape reflects lateral sediment connectivity of the landscape and has significant implications on the hydrological processes in a catchment. The main objective of this research was to quantify the lateral sediment connectivity of landslides and explore their relationship to the sediment yield of slope-channel cascades. Combining fieldwork and unmanned aerial vehicle techniques, we mapped all landslide inventories and calculated the amount of soil erosion. We proposed a new lateral sediment connectivity index (LCI), which takes into account landslide area density, landslide connectivity characteristics, and landslide potential transport distance, to explore whether landslides that occurred under heavy rainstorms affect the sediment yield in slope-channel cascades through legacy effects. Results showed that the area of landslides ranged from 0.2 m2 to 1100.07 m2, and the soil erosion amount of landslides ranged from 0.21 t to 1690.81 t during the rainstorm that occurred on 4 August. 2020 with a cumulative rainfall of 216.4 mm. The LCI of landslides was more closely related to the sediment yield of slope-channel cascades after the rainstorm. It was indicated that LCI is a good index to quantify the effect of lateral sediment connectivity of landslides on sediment yield in the catchment and that landslides generated by heavy rainstorms have long-term impacts on sediment transport processes in the slope-channel cascades of the catchment. Regarding slope management, we propose to protect gully slopes by reducing lateral sediment connectivity between upslope areas and gully slopes and to protect terrace slopes by constructing boundary dikes and drainage ditches to reduce slope collapse under heavy rainstorms.

Distribution and Characteristics of Damming Landslides Triggered by 1920 M~8 Haiyuan Earthquake, NW China

Earthquake-triggered damming landslides threaten downstream residents and affect the regional landscape by disrupting water and sediment fluxes. Therefore, it is essential to study the distribution characteristics and distinctive controlling factors of earthquake-triggered damming landslides to provide a reference for treating landslide dams caused by damming landslides. This study uses the 1920 M~8 Haiyuan earthquake-triggered landslides as an example to study the characteristics and topographic effects of damming landslides in the Loess Plateau in Northwestern China. A detailed Haiyuan-earthquake-triggered damming landslide inventory was established. The statistics of terrain, geology, seismic factors, and information gain rankings were used to quantify the significance of the controlling factors. The aspect ratio, equivalent coefficient of friction, area, and slope position was calculated. Damming landslides’ distinctive geomorphic and morphological characteristics were summarized through comparisons with non-damming landslides. The results showed that damming landslides were concentrated in areas with thick loess sediment, low relief, and close proximity to a river. Loess thickness was the most critical control factor among them. Damming landslides have the geomorphological characteristics of a large ratio of length to width (L/W), a low ratio of height to length (H/L), large scales, and entire-slope failure. Moreover, damming landslides can transform the topography of the Loess Plateau through their long-term effects. These findings highlight the characteristics of damming landslides in the Loess Plateau and supplement the global landslide dam inventory. They provide a reference for assisting in earthquake-triggered damming landslides treatments in the Loess Plateau.

ON THE EFFECT OF DEM QUALITY FOR LANDSLIDE SUSCEPTIBILITY MAPPING

Abstract. Generating precise and up-to-date landslide susceptibility maps (LSMs) in landslide-prone areas is important to identify hazard potential in the future. The data quality and the method selection affect the accuracy of the LSMs. In this context, the accuracy and precision of the digital elevation models (DEMs) used as input are among the most important performance elements. Therefore, the influence of DEM accuracy and spatial resolution in producing LSMs was investigated here. A high accuracy DEM with 5 m grid spacing produced from aerial photographs and the EU-DEM v1.1 freely accessible from Copernicus Land Monitoring Service with 25 m spatial resolution were used for producing two different LSMs using the Random Forest (RF) method in this study. The RF method has proven success for this purpose. A total of eight conditioning factors, which include topographical and geological features, was used as model input. The landslide inventory was derived with the help of aerial stereo images with 20 cm and 30 cm ground sampling distances. The performances of the LSMs were assessed with receiver operating characteristics (ROC) area under curve (AUC) values. In addition, the results were compared with visual inspection. The results show that although the AUC values obtained from the aerial DEM (0.95) and EU-DEM v1.1 (0.93) were comparable; based on the visual assessments, the LSM obtained from the higher resolution DEM was found more successful in detecting the landslides and thus exhibited better prediction performance.

Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County

Landslides are one of the most widespread disasters and threaten people’s lives and properties in many areas worldwide. Landslide susceptibility mapping (LSM) plays a crucial role in the evaluation and extenuation of risk. To date, a large number of machine learning approaches have been applied to LSM. Of late, a high-level convolutional neural network (CNN) has been applied with the intention of raising the forecast precision of LSM. The primary contribution of the research was to present a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM), logistic regression (LR), and random forest (RF). Subsequently, we used this model in the Wenchuan region, where a catastrophic earthquake happened on 12 May 2008 in China. There were 405 valuable landslides in the landslide inventory, which were divided into a training set (283 landslides) and validation set (122 landslides). Furthermore, 11 landslide causative factors were selected as the model’s input, and each model’s output was reclassified into five intervals according to the sensitivity. We also evaluated the model’s performance by the receiver operating characteristic (ROC) curve and several statistical metrics, such as precision, recall, F1-score, and other measures. The results indicated that the CNN-based methods achieved the best performance, with the success-rate curve (SRC) and prediction-rate curve (PRC) approaches reaching 93.14% and 91.81%, respectively. The current research indicated that the approach based on the CNN for LSM had both outstanding goodness-of-fit and excellent prediction capability. Generally, the LSM in our research is capable of advancing the ability to assess landslide susceptibility.

Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape

Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides triggered by a summer rainstorm in Jølster (Norway), based on changes in the normalised difference vegetation index (NDVI) between pre- and post-event images. Pre- and post-event multi-temporal images were then created by reducing across all available images within one month before and after the landslide events, from which final change detection image composites were produced. We used the mean of backscatter intensity in co- (VV) and cross-polarisations (VH) for Sentinel-1 synthetic aperture radar (SAR) data and maximum NDVI for Sentinel-2. The NDVI-based mapping increased the number of registered events from 14 to 120, while spatial bias was decreased, from 100% of events located within 500 m of a road to 30% close to roads in the new inventory. Of the 120 landslides, 43% were also detectable in the multi-temporal SAR image composite in VV polarisation, while only the east-facing landslides were clearly visible in VH. Noise, from clouds and agriculture in Sentinel-2, and speckle in Sentinel-1, was reduced using the multi-temporal composite approaches, improving landslide visibility without compromising spatial resolution. Our results indicate that manual or automated landslide detection could be significantly improved with multi-temporal image composites using freely available earth observation images and Google Earth Engine, with valuable potential for improving spatial bias in landslide inventories. Using the multi-temporal satellite image composites, we observed significant improvements in landslide visibility in Jølster, compared with conventional bi-temporal change detection methods, and applied this for the first time using VV-polarised SAR data. The GEE scripts allow this procedure to be quickly repeated in new areas, which can be helpful for reducing spatial bias in landslide databases.

Event-based landslide susceptibility models in Shihmen watershed, Taiwan: accounting for the characteristics of rainfall events.

When performing a landslide susceptibility analysis, a model is usually established on the basis of a multi-temporal or event-triggered landslide inventory. Because multi-temporal landslide inventories for most areas are rarely available, an event-triggered landslide inventory is often used, but the result depends on the selection of single event. In order to establish a landslide susceptibility model with a good prediction performance, the present study tried to find out how to select a single event-triggered landslide inventory, and investigated the effect of various combinations of event inventories. We selected Shihmen reservoir watershed as the research area, conducted a logistic regression analysis to build 23 event-based landslide susceptibility models and one multi-year landslide susceptibility model, and estimated the performance of these models. In addition, this study further assessed the influence of event characteristics on the model prediction performance, used the above results to merge two different events, and then established models based on these combinations. The results indicated that when establishing an event-based landslide susceptibility model, selecting events with suitable rainfall return periods and landslide density can yield robust models with relatively high predictive ability. Furthermore, the combination of two events which negatively correlate with each other in rainfall spatial distributions can enhance a model's predictive ability and modeling efficiency.

Landslide hazard assessment using analytic hierarchy process (AHP): A case study of National Highway 5 in India

Slope failure along highways is a crucial problem in hilly regions. Landslide hazard maps are very efficient and effective tools for planning and management of landslide disasters. Aim of this study is to prepare a landslide hazard map along national highway 5 (197.600–283.200 Km) using analytic hierarchy process (AHP) model. The different causative factors of landslides considered in this study are slope, aspect, curvature, relative relief, fault density, drainage density, geology, topographic wetness index (TWI), distance from road and lithology. The causative factors are divided into sub-factors and weightage are assigned according to analytic hierarchy process (AHP). The causative factor layers are overlaid using weighted linear combination (WLC) technique and a landslide hazard map is prepared. A landslide inventory of 215 landslides is used for validation of the landslide hazard map. The map shows a prediction rate of 0.825 on area under curve (AUC) technique. The study can be used by the construction planners and decision makers.

Inventory of large landslides along the Central Western Andes (ca. 15°–20° S): Landslide distribution patterns and insights on controlling factors

The western flank of the Central Andes hosts some of the largest terrestrial landslides (v > km3), which morphologies are particularly well-preserved due to low erosion rates related to the hyper-arid climate prevailing in this region since the Miocene. First-order questions are pending about the factors controlling the development and the triggering of those large-scale slope failures. Previous studies provided some geomorphological analysis and dating on individual study cases, but a regional-scale vision of landslide processes long the Central Western Andes is missing.Here we report an original inventory of large landslides (areas from 0.1 to 180 km2) established along the western flank of the Central Andes between latitudes ca. 15 and 20° S, and from the Pacific coast to the Altiplano. Based on manual mapping (using satellite images analysis, Google Earth and DEMs analysis) and a compilation of previous works, we inventoried more than a thousand large landslides in this region. We then statistically explored the database according to the landslides typology, size, abundance and relation to geologic, tectonic and climatic settings of the Central Western Andes in order to provide a first insight on their controlling factors. Landslide size-frequency distribution follows a power-law with an exponent of 2.31 ± 0.16 and a cut-off of 4.0 ± 1.9 km2 showing a strong contribution of the largest landslides to the cumulated landslide area. We revealed a dominance of rockslide typology (86%) characterized by in-mass slides, the rest being rock-avalanche type (14%) marked by typical granular-flow morphologies. Combination of specific lithology and great local relief emerge as favorable conditioning factor for large landslide initiation, in particular in the case of river incisions though ignimbrites of the Paleogene-Neogene (Huaylillas Formation), concentrating >30% of the landslides. Moreover, landslide clusters tend to follow crustal faults networks suggesting a long-term control of tectonic activity. Most of the identified landslides are paleo events. We tentatively argue that their triggering could not have been possible in the current hyper-arid conditions of the Atacama Desert and its periphery. Future research providing dating on some of the landslide clusters identified in this study is needed to explore possible temporal correlations between periods of landslide activity and external seismic and/or climatic cycles.

A Scientometric Analysis of Predicting Methods for Identifying the Environmental Risks Caused by Landslides

The effects of a landslide can represent a very big problem, including the death of people, damage to the land, environmental pollution and the loss of natural resources. Landslides are the most important medium for transferring sediments and polluting waterways by earth and organic materials. An excess of sediments reduces the quality of fish habitat and the potability of water. In order to understand landslides in depth, a thorough study was conducted using a scientometric analysis, as well as a thorough practical examination of landslide analysis and monitoring techniques. This review focused on methods used for landslide analysis, including physical models requiring easily prepared event-based landslide inventory, probabilistic methods which are useful for both shallow and earthquake-based landslides, and landslide monitoring performed by remote sensing techniques, which provide data helpful for prediction, monitoring and mapping. The fundamental principles of each method are described in terms of the method used, and its advantages, and limits. People and infrastructure are at danger from landslides caused by heavy rain, so this report highlights landslide-prone regions and considers the analysis methods for landslides used in these countries, with a view to identifying mitigation measures for coping with landslide risks in hilly areas. Furthermore, future landslide research possibilities, as well as possible modeling methods, are addressed. The report summarizes some landslide prediction and monitoring techniques used in landslide-prone countries which can help inform researchers seeking to protect the public from danger in landslide areas.

Zonation of Landslide Hazards Based on Frequency Ratio Modeling at Coonoor and Kothagiri Taluks of Nilgiri District, Tamil Nadu, India

A frequency ratio model is developed within the framework of a Geographic Information System (GIS) to map landslide hazards in a mountainous environment. The Nilgiri Hills (Mountains) of Tamil Nadu are prone to landslides; these landslides are typically the result of the structural failure of thick laterite soils that have been saturated by heavy rains during the monsoon season. The GIS based analysis in the present work incorporates the frequency ratio values from the existing landslides. The landslide inventory of paleoscars was located by a collection of existing historical reports. Detailed field surveys have been carried out in the study area. In the observation, 118 landslide locations were identified and mapped by GIS; out of that, 83 (70 %) locations were randomly selected for the training data set and the remaining 35 (30 %) cases were used for the model validation purposes. For spatial modeling, the landslide triggering parameters include active-passive slope, concave-plain-convex slope, drainage density, dissected-undissected slope, geology, geomorphology, lineament density, lineament frequency, lineament intersection density, land use / land cover, rainfall, regolith cover, shallow-moderate-steep slope, soil and water level for landslide hazard zonation. This model has been used to spatially classify the study area into three zones viz; high, moderate and low landslide hazard zones. The frequency ratio model was validated with the existing landslide location data using R-index method. The validation of the results showed that the values is 1115.23 in R-index as high hazard zone when R-index increases with the level of susceptibility. The obtained results will provide a basis for planning, risk reduction strategies and various developmental activities in the Nilgiri area.

Landslide Susceptibility Mapping Using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

Landslide is one of the most devastating hazards in Chattagram Dsitrict and has become a recurrent phenomenon in this region. This study attempts to produce Landslide Susceptibility Map (LSM) for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). Landslide Inventory (2001–2017) of Chittagong Hilly Areas database was used to measure the relationship between the previous landslides with the landslide conditioning factors. SRTM DEM and Landsat satellite images were collected from USGS and the geological data were collected from GSB to produce the thematic layer of conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.