Developing a Landslide Susceptibility Map Using the Analytic Hierarchical Process in Ta Van and Hau Thao Communes, Sapa, Vietnam

Landslides are considered one of the most serious problems in the mountainous regions of the northern part of Vietnam due to the special topographic and geological conditions associated with the occurrence of tropical storms, steep slopes on hillsides, and human activities. This study initially identified areas susceptible to landslides in Ta Van Commune, Sapa District, Lao Cai Region using Analytical Hierarchy Analysis. Ten triggering and conditioning parameters were analyzed: elevation, slope, aspect, lithology, valley depth, relief amplitude, distance to roads, distance to faults, land use, and precipitation. The consistency index (CI) was 0.0995, indicating that no inconsistency in the decision-making process was detected during computation. The consistency ratio (CR) was computed for all factors and their classes were less than 0.1. The landslide susceptibility index (LSI) was computed and reclassified into five categories: very low, low, moderate, high, and very high. Approximately 9.9% of the whole area would be prone to landslide occurrence when the LSI value indicated at very high and high landslide susceptibility. The area under curve (AUC) of 0.75 illustrated that the used model provided good results for landslide susceptibility mapping in the study area. The results revealed that the predicted susceptibility levels were in good agreement with past landslides. The output also illustrated a gradual decrease in the density of landslide from the very high to the very low susceptible regions, which showed a considerable separation in the density values. Among the five classes, the highest landslide density of 0.01274 belonged to the very high susceptibility zone, followed by 0.00272 for the high susceptibility zone. The landslide susceptibility map presented in this paper would help local authorities adequately plan their landslide management process, especially in the very high and high susceptible zones.

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Landslide susceptibility mapping of SE Serbia using GIS

Geoloski anali Balkanskog poluostrva ◽

10.2298/gabp1902105d ◽

2019 ◽

Vol 80 (2) ◽

pp. 105-116

Author(s):

Sonja Djokanovic

Keyword(s):

Landslide Susceptibility ◽

Slope Angle ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Susceptibility Assessment ◽

High Susceptibility ◽

Landslide Susceptibility Map ◽

Analytic Hierarchy ◽

Hierarchy Process ◽

Very High

Landslides represent a great problem in Serbia. According to current estimates 30-35 % of Serbia is affected by landslides. In this paper a landslide susceptibility analysis is done for SE Serbia. Study area covers 1507 km2. Relief is hilly or mountainous and characterized by high altitude differences. Analysis is done by geographic information system (GIS) and evaluation by analytic hierarchy process (AHP). For susceptibility assessment are used four factors: lithology, slope angle, distance to rivers and distance to faults. The most landslides are formed on slope steepness less than 30?. There is four classes of susceptibility in study area. Zone of very high susceptibility make 63.9 % of the study area. Zone of high susceptibility covers 15.7 % of the study area. The moderate class occupies 37.4% and zone classified as having low susceptibility accounts for 10 % of study area. Final landslide susceptibility map of SE Serbia is satisfactory.

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Geospatial Technology for Landslide Susceptibility Mapping along the Vathalmalai Ghat road section, South India

Journal of Geology Geography and Geoecology ◽

10.15421/112163 ◽

2021 ◽

Vol 30 (4) ◽

pp. 683-691

Author(s):

G. Kavitha ◽

S. Anbazhagan ◽

S. Mani

Keyword(s):

Landslide Susceptibility ◽

Drainage Density ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Susceptibility Map ◽

Landslide Susceptibility Zonation ◽

Road Section ◽

Gps Measurement ◽

Causative Factors ◽

Very High

Landslides are among the most prevalent and harmful hazards. Assessment of landslide susceptibility zonation is an important task in reducing the losses of lifeand properties. The present study aims to demarcate the landslide prone areas along the Vathalmalai Ghat road section (VGR) using remote sensing and GIS techniques. In the first step, the landslide causative factors such as geology, geomorphology, slope, slope aspect, land use / land cover, drainage density, lineament density, road buffer and relative relief were assessed. All the factors were assigned to rank and weight based on the slope stability of the landslide susceptibility zones. Then the thematic maps were integrated using ArcGIS tool and landslide susceptibility zonation was obtained and classified into five categories ; very low, low, moderate, high and very high. The landslide susceptibility map is validated with R-index and landslide inventory data collected from the field using GPS measurement. The distribution of susceptibility zones is ; 16.5% located in very low, 28.70% in low, 24.70% in moderate, 19.90% in high and 10.20% in very high zones. The R-index indicated that about 64% landslide occurences correlated with high to very high landslide susceptiblity zones. The model validation indicated that the method adopted in this study is suitable for landslide disaster mapping and planning.

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Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

Applied Sciences ◽

10.3390/app10186335 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6335 ◽

Cited By ~ 3

Author(s):

Kamila Pawluszek-Filipiak ◽

Natalia Oreńczak ◽

Marta Pasternak

Keyword(s):

Landslide Susceptibility ◽

Research Question ◽

Susceptibility Mapping ◽

Landslide Inventory ◽

Landslide Susceptibility Mapping ◽

High Susceptibility ◽

Geological Structures ◽

Area Index ◽

Landslide Occurrence ◽

Very High

To mitigate the negative effects of landslide occurrence, there is a need for effective landslide susceptibility mapping (LSM). The fundamental source for LSM is landslide inventory. Unfortunately, there are still areas where landslide inventories are not generated due to financial or reachability constraints. Considering this led to the following research question: can we model landslide susceptibility in an area for which landslide inventory is not available but where such is available for surrounding areas? To answer this question, we performed cross-modeling by using various strategies for landslide susceptibility. Namely, landslide susceptibility was cross-modeled by using two adjacent regions (“Łososina” and “Gródek”) separated by the Rożnów Lake and Dunajec River. Thus, 46% and 54% of the total detected landslides were used for the LSM in “Łososina” and “Gródek” model, respectively. Various topographical, geological, hydrological and environmental landslide-conditioning factors (LCFs) were created. These LCFs were generated on the basis of the Digital Elevation Model (DEM), Sentinel-2A data, a digitized geological and soil suitability map, precipitation, the road network and the Różnów lake shapefile. For LSM, we applied the Frequency Ratio (FR) and Landslide Susceptibility Index (LSI) methods. Five zones showing various landslide susceptibilities were generated via Natural Jenks. The Seed Cell Area Index (SCAI) and Relative Landslide Density Index were used for model validation. Even when the SCAI indicated extremely high values for “very low” susceptibility classes and very small values for “very high” susceptibility classes in the training and validation areas, the accuracy of the LSM in the validation areas was significantly lower. In the “Łososina” model, 90% and 57% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. In the “Gródek” model, 86% and 46% of the landslides fell into the “high” and “very high” susceptibility zones in the training and validation areas, respectively. Moreover, the comparison between these two models was performed. Discrepancies between these two models exist in the areas of critical geological structures (thrust and fault proximity), and the reliability for such susceptibility zones can be low (2–3 susceptibility zone difference). However, such areas cover only 11% of the analyzed area; thus, we can conclude that in remaining regions (89%), LSM generated by the inventory for the surrounding area can be useful. Therefore, the low reliability of such a map in areas of critical geological structures should be borne in mind.

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Landslide susceptibility mapping using Weight of Evidence Method in Haku, Rasuwa District, Nepal

Journal of Nepal Geological Society ◽

10.3126/jngs.v58i0.24601 ◽

2019 ◽

Vol 58 ◽

pp. 163-171 ◽

Cited By ~ 4

Author(s):

Arishma Gadtaula ◽

Subodh Dhakal

Keyword(s):

Landslide Susceptibility ◽

Google Earth ◽

Weight Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Mountainous Area ◽

High Susceptibility ◽

Seismic Landslides ◽

2015 Gorkha Earthquake ◽

Very High

The 2015 Gorkha Earthquake resulted in many other secondary hazards affecting the livelihoods of local people residing in mountainous area. Plenty of earthquake induced landslides and mass movement activities were observed after earthquake. Haku region of Rasuwa was also one of the severely affected areas by co-seismic landslides triggered by the disastrous earthquake. Statistics shows that around 400 families were relocated from Haku Post-earthquake (MoFA, 2015). A total of 101 co-seismic landslides were focused during the study and were verified during the fieldwork in Haku village. The conditioning factors used in this study were slope, aspect, elevation, curvature (plan and profile), landuse, geology and PGA. The conditioning factor maps were prepared in GIS working environment and further analysis was conducted with the assistance of Google earth. This study used Weight of Evidence (WoE), a bivariate statistical model and its performance was assessed. The susceptibility map was further characterized into five different classes namely very low, low, high, medium and very high susceptibility zones. The statistical analysis obtained from the results of the susceptibility map prepared by using WoE model gave the results that maximum area percentage of landslide distribution was observed in medium and high susceptibility classes i.e. 38% and 33% followed by very high (13%), low (10%) and very low classes (5.8%) About 25% of the total landslides are separated to validate the prepared model used in the landslide susceptibility zonation. The overlay method predicts the reliability of the model.

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A new approach in the development and analysis of the landslide susceptibility map of the hillslopes of Bujumbura, Burundi

EUREKA Physics and Engineering ◽

10.21303/2461-4262.2021.001724 ◽

2021 ◽

pp. 26-34

Author(s):

Desire Kubwimana ◽

Lahsen Ait Brahim ◽

Abdellah Abdelouafi

Keyword(s):

Landslide Susceptibility ◽

Slope Angle ◽

Landslide Inventory ◽

Slope Aspect ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Aerial Photos ◽

Conditioning Factors ◽

Mountainous Regions ◽

Tropical Environments

As in other hilly and mountainous regions of the world, the hillslopes of Bujumbura are prone to landslides. In this area, landslides impact human lives and infrastructures. Despite the high landslide-induced damages, slope instabilities are less investigated. The aim of this research is to assess the landslide susceptibility using a probabilistic/statistical data modeling approach for predicting the initiation of future landslides. A spatial landslide inventory with their physical characteristics through interpretation of high-resolution optic imageries/aerial photos and intensive fieldwork are carried out. Base on in-depth field knowledge and green literature, let’s select potential landslide conditioning factors. A landslide inventory map with 568 landslides is produced. Out of the total of 568 landslide sites, 50 % of the data taken before the 2000s is used for training and the remaining 50 % (post-2000 events) were used for validation purposes. A landslide susceptibility map with an efficiency of 76 % to predict future slope failures is generated. The main landslides controlling factors in ascendant order are the density of drainage networks, the land use/cover, the lithology, the fault density, the slope angle, the curvature, the elevation, and the slope aspect. The causes of landslides support former regional studies which state that in the region, landslides are related to the geology with the high rapid weathering process in tropical environments, topography, and geodynamics. The susceptibility map will be a powerful decision-making tool for drawing up appropriate development plans in the hillslopes of Bujumbura with high demographic exposure. Such an approach will make it possible to mitigate the socio-economic impacts due to these land instabilities

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Landslide Susceptibility Mapping Based on the Combination of Bivariate Statistics and Modified Analytic Hierarchy Process Methods: A Case Study of Tinh Tuc Town, Nguyen Binh District, Cao Bang Province, Vietnam

Journal of Disaster Research ◽

10.20965/jdr.2021.p0521 ◽

2021 ◽

Vol 16 (4) ◽

pp. 521-528

Author(s):

Nguyen Trung Kien ◽

The Viet Tran ◽

Vy Thi Hong Lien ◽

Pham Le Hoang Linh ◽

Nguyen Quoc Thanh ◽

...

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Water Storage ◽

Landslide Inventory ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

The Road ◽

Hierarchy Process ◽

Very High

Tinh Tuc town, Cao Bang province, Vietnam is prone to landslides due to the complexity of its climatic, geological, and geomorphological conditions. In this study, in order to produce a landslide susceptibility map, the modified analytical hierarchy process and landslide susceptibility analysis methods were used together with the layers, including: landslide inventory, slope, weathering crust, water storage, geology, land use, and distance from the road. In the study area, 98% of landslides occurred in highly or completely weathered units. Geology, land use, and water storage data layers were found to be important factors that are closely related with the occurrence of landslides. Although the weight of the “distance from the road” factor has a low value, the weight of layer “<100 m” has a high value. Therefore, the landslide susceptibility index very high is concentrated along the roads. For the validation of the predicted result, the landslide susceptibility map was compared with the landslide inventory map containing 47 landslides. The outcome shows that about 90% of these landslides fall into very high susceptibility zones.

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Evaluating the impact of road construction on landslide susceptibility- A case study of Mandi district, Himachal Pradesh, India.

10.22541/au.162132838.80285009/v1 ◽

2021 ◽

Author(s):

Amol Sharma ◽

Chander Prakash

Keyword(s):

Statistical Models ◽

Landslide Susceptibility ◽

Road Construction ◽

Himachal Pradesh ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

The Impact ◽

Very High

Landslide susceptibility mapping has proved to be crucial tool for effective disaster management and planning strategies in mountainous regions. The present study is perused to investigate the changes in the landslide susceptibility of the Mandi district of Himachal Pradesh due to road construction. For this purpose, an inventory of 1723 landslides was generated from various sources. Out of these, 1199 (70%) landslides were taken in the training dataset to be used for modelling and prediction purposes, while 524 (30%) landslides were taken in the testing dataset to be used for validation purposes. Eleven landslide causative factors were selected from numerous hydrological, geological and topographical factors and were analyzed for landslide susceptibility mapping using three bivariate statistical models, namely; Frequency Ratio (FR), Certainty Factor (CF) and Shanon Entropy (SE). Two sets of LSM maps i.e. landslide susceptibility map natural (LSMN) and landslide susceptibility map road (LSMR), were generated using the above mentioned bivariate models and were divided into five landslide susceptibility classes namely; very low, low, medium, high and very high. These maps were analyzed for accuracy of prediction and validation using receiver operating characteristic (ROC) curves and area under curve (AUC) technique which indicated that all three bivariate statistical models performed satisfactorily with the SE model had the highest prediction and validation accuracy of 83-86%. Further analysis LSM maps confirmed that the percentage area in high and very high classes of land-slide susceptibility increased by 2.67-4.17% due to road construction activities in the study area.

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Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM

Symmetry ◽

10.3390/sym12061047 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1047 ◽

Cited By ~ 3

Author(s):

Chenglong Yu ◽

Jianping Chen

Keyword(s):

Influencing Factors ◽

Landslide Susceptibility ◽

Jilin Province ◽

Landslide Susceptibility Mapping ◽

Support Vector ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Slope Unit ◽

Svm Model ◽

Very High

The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city.

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Implementing Landslide Susceptibility Map at Watershed Scale of Lompobattang Mountain South Sulawesi, Indonesia

Indonesian Journal of Geography ◽

10.22146/ijg.16632 ◽

2018 ◽

Vol 50 (2) ◽

pp. 197

Author(s):

Abdul Rachman Rasyid ◽

Netra Prakash Bhandary ◽

Ryuichi Yatabe

Keyword(s):

Land Use ◽

Landslide Susceptibility ◽

Disaster Mitigation ◽

Weight Of Evidence ◽

Slope Aspect ◽

Susceptibility Map ◽

Watershed Scale ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Predictive Rate

This study attempts to predict future landslide occurrence at watershed scale and calculate the potency of landslide for each sub-watershed at Lompobatang Mountain. In order to produce landslide susceptibility map (LSM) using the statistical model on the watershed scale, we identified the landslide with landslide inventories that occurred in the past, and predict the prospective future landslide occurrence by correlating it with landslide causal factors. In this study, six parameters were used namely, distance from fault, slope, aspect, curvature, distance from river and land use. This research proposed the weight of evidence (WoE) model to produce a landslide susceptibility map. Success and predictive rate were also used to evaluate the accuracy by using Area under curve (AUC) of Receiver operating characteristic (ROC). The result is useful for land use planner and decision makers, in order to devise a strategy for disaster mitigation.

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Landslide susceptibility mapping along Tulsipur-Kapurkot road section and its surrounding region using bivariate statistical model

Journal of Nepal Geological Society ◽

10.3126/jngs.v50i1.22868 ◽

2016 ◽

Vol 50 (1) ◽

pp. 83-93

Author(s):

Khagendra Poudel ◽

Amar Deep Regmi

Keyword(s):

Land Use ◽

Statistical Model ◽

Landslide Susceptibility ◽

Landslide Susceptibility Mapping ◽

Slope Aspect ◽

Small Scale ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

The Road ◽

Road Section

The Tulsipur-Kapurkot road is the main highway connecting the northern part of Rapti zone to the rest of Nepal. It suffers from numerous mass movements obstructing the traffic every monsoon. This paper describes the development of landslide susceptibility map of the road section and its surrounding regions based on bivariate (frequency ratio) statistical model. Geologically, the road section passes through the rocks of Lesser Himalaya, Siwaliks and Quaternary deposits. Several large and small scale thrusts present within the area making it unstable. For the susceptibility evaluation of the region, first a landslide inventory map consisting more than 187 landslides was prepared. These landslide locations were then randomly partitioned into a ratio of 80/20 for training and validating the models. Second, nine landslide causative factors were prepared. They include slope, aspect, elevation, curvature, geology, land use, distance from fault, distance from river and distance from major road sections. Finally, a landslide susceptibility map of the region was obtained and it was validated using area under curve (AUC). From the analysis, the success rate of the model is found to be 85.18% and predictive accuracy is 78.76%. The resultant susceptibility map shows that the highway in between Ranagaun to Khamari and Ramri to Kapurkot falls within very high to high susceptible zone. Besides, it is observed that the Kapurkot Bazar is also under high landslide susceptible zone. Furthermore, the northern part of the watershed lies in high landslide susceptible zone. The result of this study is useful for land use planning and decision making in landslide management activities.

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