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

Road Construction ◽

Himachal Pradesh ◽

Susceptibility Mapping ◽

Susceptibility Map ◽

The Impact ◽

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.

Application of Airborne LiDAR-Derived Parameters and Probabilistic-Based Frequency Ratio Model in Landslide Susceptibility Mapping

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.225.442 ◽

2012 ◽

Vol 225 ◽

pp. 442-447 ◽

Cited By ~ 4

Author(s):

Biswajeet Pradhan ◽

Zulkiflee Abd. Latif ◽

Siti Nur Afiqah Aman

Keyword(s):

High Resolution ◽

Frequency Ratio ◽

Susceptibility Mapping ◽

Airborne Lidar ◽

Susceptibility Map ◽

Ratio Model ◽

Frequency Ratio Model

The escalating number of occurrences of natural hazards such as landslides has raised a great interest among the geoscientists. Due to the extremely high number of point’s returns, airborne LiDAR permits the formation of more accurate DEM compared to other space borne and airborne remote sensing techniques. This study aims to assess the capability of LiDAR derived parameters in landslide susceptibility mapping. Due to frequent occurrence of landslides, Ulu Klang in Selangor state in Malaysia has been considered as application site. A high resolution of airborne LiDAR DEM was constructed to produce topographic attributes such as slope, curvature and aspect. These data were utilized to derive secondary deliverables of landslide parameters such as topographic wetness index (TWI), surface area ratio (SAR) and stream power index (SPI). A probabilistic based frequency ratio model was applied to establish the spatial relationship between the landslide locations and each landslide related factors. Subsequently, factor ratings were summed up to yield Landslide Susceptibility Index (LSI) and finally a landslide susceptibility map was prepared. To test the model performance, receiver operating characteristics (ROC) curve was carried out together with area under curve (AUC) analysis. The produced landslide susceptibility map demonstrated that high resolution airborne LiDAR data has huge potential in landslide susceptibility mapping.

GIS based Landslides Susceptibility Mapping using Probabilistic Certainty Factor for Hazard Zonation in Mandakini Valley

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.f7288.038620 ◽

2020 ◽

Vol 8 (6) ◽

pp. 398-404

Keyword(s):

High Risk ◽

Catchment Area ◽

Susceptibility Mapping ◽

Susceptibility Map ◽

Hazard Zonation ◽

Certainty Factor ◽

Risk Zonation

Landslides are highly threatening a phenomenon which is very common in hilly region and mountainous regions. These landslides trigger major risks leading to heavy losses in terms of life and property. Many studies were conducted globally to determine Landslide vulnerability of different locations. In order to assess vulnerability, there were few studies around Landslides Susceptibility mapping also whose main objective is to identify high-risk vulnerable areas, there by applying measure to reduce the damage caused, if it were to happen in near future. In literature, there are many methods available for predictive susceptibility mapping of landslides. However, identification of any of the prevalent method for a specific area require utmost care and prudence because land sliding is a result of complex geo-environmental spatial factors. Mandakini valley is highly ruggedized terrain with intensive rains during monsoon season. As a result, Landslides are very common in the Mandakini River valley and its catchment area. These landslides cause severe damage to human settlements and infrastructure present in this area. In this study, we have used certainty factor method in order to generate landslide susceptibility map for the catchment area of Mandakini river. Certainty factor approach is a bi-variate probabilistic method which uses Geo-environmental parameters like elevation, slope, aspect, rainfall distance away from river, soil characteristics etc. to generate landslide susceptibility map. A Script was developed in ArcPy - a python package to design tools for generating susceptibility map. These tools can run both at desktop level and at server level and generate results in an integrated way. Esri ArcMap 10.7 is used in order to generate required data layers and thematic maps. Overall, this paper leverages GIS technology and its tools to performs Landslide Susceptibility Mapping using Probabilistic Certainty Factor and generate Hazard Zonation of Mandakini Valley using an automated script for generating Landslide Susceptibility Mapping and Hazard Risk Zonation. It was found that out of 696, total 136 villages are under high risk of landsides, total 329 villages are under moderate risks and around 231 villages are under low risk zonation impacting lives of approx. 216166 people. Also, it is worth mentioning that a GIS based script was developed to automate generation of Landslide Susceptibility Maps which can be used where the same geological and topographical feature prevails.

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 ◽

Water Storage ◽

Landslide Inventory ◽

Susceptibility Map ◽

The Road ◽

Hierarchy Process ◽

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.

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

Journal of Disaster Research ◽

10.20965/jdr.2021.p0529 ◽

2021 ◽

Vol 16 (4) ◽

pp. 529-538

Author(s):

Thi Thanh Thuy Le ◽

The Viet Tran ◽

Viet Hung Hoang ◽

Van Truong Bui ◽

Thi Kien Trinh Bui ◽

...

Keyword(s):

Slope Aspect ◽

Susceptibility Map ◽

High Susceptibility ◽

Landslide Occurrence ◽

Mountainous Regions ◽

Geological Conditions ◽

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.

Landslide susceptibility mapping using GIS along the Niš-North Macedonia highway

Zbornik radova Departmana za geografiju turizam i hotelijerstvo ◽

10.5937/zbdght2101001t ◽

2021 ◽

pp. 1-13

Author(s):

Dajana Tešić

Keyword(s):

Susceptibility Mapping ◽

Susceptibility Map ◽

Cultural Goods ◽

Relative Significance ◽

Industrial Facilities ◽

The Subject ◽

The Republic ◽

Landslide events are a serious challenge worldwide, as well as in the Republic of Serbia. According to current estimates, 25-30% of the territory of Serbia is endangered by landslides. Due to intensive landslides, residential, infrastructural, energy, water management and industrial facilities, as well as natural and cultural goods are endangered. The subject of this paper is the use of GIS for the production of a landslide susceptibility map (LSM) in the area of the corridor of the highway E-75, section Niš - border of the Republic of Northern Macedonia. Emphasis is placed on the analysis of certain factors that influence the landslide events, as well as on the modifiers of the process itself. In this paper the following factors were selected for the assessment and susceptibility mapping: lithology, pedological factors, precipitation, aspect, slope, distance from watercourses, land use and distance from roads. The AHP method was then used to determine the relative significance and priority of predisposing factors. The layers were overlapped using ArcGISPro software. The results show that 17.2% of the surface is not susceptible (very low and low susceptibility), and 31.9% of the surface is prone to landslide events (high and very high susceptibility). The remaining 49.1% of the area belongs to the area of moderate susceptibility. Adding to this dense road network in the study area, as well as a large number of populated places, it can be concluded that the damage from the activation of the landslide would be significant. The results suggest that 60.7 km of the highway is in an area of high and greatest risk of landslides.

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 ◽

Jilin Province ◽

Support Vector ◽

Susceptibility Map ◽

Slope Unit ◽

Svm Model ◽

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.

Landslide Susceptibility Mapping using Statistical Methods in Uatzau Catchment Area, Northwestern Ethiopia

10.21203/rs.3.rs-15731/v1 ◽

2020 ◽

Author(s):

Azemeraw Wubalem

Keyword(s):

Prediction Accuracy ◽

Susceptibility Mapping ◽

Information Value ◽

Susceptibility Map ◽

Ratio Model ◽

Certainty Factor ◽

Susceptibility Model

Abstract Landslide susceptibility mapping is important to hazard management and to have planning development activities in the mountainous country like Ethiopia. In the present study, the landslide susceptibility mapping of the Uatzau basin is made using certainty factor, information value and logistic regression methods. Preparation of landslide inventory map from detailed fieldwork and Google Earth image interpretation was the first activity. Thus, 514 landslides were mapped and out of which 490 (70%) of landslides were randomly selected keeping their spatial distribution to build landslide susceptibility models while the remaining 155 (30%) of the landslides were used to models validation. It is clear that the effectiveness of the landslide susceptibility model using GIS and statistical methods is depending on the selection of the causative factors, which have a prevailing effect on landslide occurrence. In this study, six factors including lithology, land use/cover, distance to stream, slope gradient, slope aspect, and slope curvature were the landslide factors that were evaluated. After preparation of these factor maps, the effects of them on slope instability was determined by comparing with landslide inventory raster map using GIS. Finally, the landslide susceptibility model for the Uatzau area was developed and validated using the receiver operating characteristics curve (ROC). The results of ROC showed that for landslide susceptibility map using frequency ratio model (FRM) with an AUC value of 0.8883 has the highest prediction accuracy of 88.83%. The landslide susceptibility map, which is produced using Certainty factor and information value methods also showed that 87.03% and 84.83% of prediction accuracy respectively. Besides the prediction accuracy of the model, the success rate curve for all models was applied and the result showed that more than 80% accuracy (i.e, 80.83% for the information value model, 87.19% for the certainty factor model and 83.27% for frequency ratio model). The present research finds out that all methods/ models, which have employed in this study showed that reasonably very good accuracy in predicting landslide susceptibility of the Uatzau area. Therefore, these landslide susceptibility maps can be used for regional land use planning and landslide hazard mitigation purposes.

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):

Slope Angle ◽

Susceptibility Map ◽

Susceptibility Assessment ◽

High Susceptibility ◽

Analytic Hierarchy ◽

Hierarchy Process ◽

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.

Landslide Susceptibility Mapping and Assessment Using Geospatial Platforms and Weights of Evidence (WoE) Method in the Indian Himalayan Region: Recent Developments, Gaps, and Future Directions

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030114 ◽

2021 ◽

Vol 10 (3) ◽

pp. 114

Author(s):

Amit Kumar Batar ◽

Teiji Watanabe

Keyword(s):

Land Use ◽

Susceptibility Mapping ◽

Landslide Inventory ◽

Susceptibility Map ◽

Future Directions ◽

Causative Factors ◽

Recent Developments ◽

The Himalayan region and hilly areas face severe challenges due to landslide occurrences during the rainy seasons in India, and the study area, i.e., the Rudraprayag district, is no exception. However, the landslide related database and research are still inadequate in these landslide-prone areas. The main purpose of this study is: (1) to prepare the multi-temporal landslide inventory map using geospatial platforms in the data-scarce environment; (2) to evaluate the landslide susceptibility map using weights of evidence (WoE) method in the Geographical Information System (GIS) environment at the district level; and (3) to provide a comprehensive understanding of recent developments, gaps, and future directions related to landslide inventory, susceptibility mapping, and risk assessment in the Indian context. Firstly, 293 landslides polygon were manually digitized using the BHUVAN (Indian earth observation visualization) and Google Earth® from 2011 to 2013. Secondly, a total of 14 landslide causative factors viz. geology, geomorphology, soil type, soil depth, slope angle, slope aspect, relative relief, distance to faults, distance to thrusts, distance to lineaments, distance to streams, distance to roads, land use/cover, and altitude zones were selected based on the previous study. Then, the WoE method was applied to assign the weights for each class of causative factors to obtain a landslide susceptibility map. Afterward, the final landslide susceptibility map was divided into five susceptibility classes (very high, high, medium, low, and very low classes). Later, the validation of the landslide susceptibility map was checked against randomly selected landslides using IDRISI SELVA 17.0 software. Our study results show that medium to very high landslide susceptibilities had occurred in the non-forest areas, mainly scrubland, pastureland, and barren land. The results show that medium to very high landslide susceptibilities areas are in the upper catchment areas of the Mandakini river and adjacent to the National Highways (107 and 07). The results also show that landslide susceptibility is high in high relative relief areas and shallow soil, near thrusts and faults, and on southeast, south, and west-facing steep slopes. The WoE method achieved a prediction accuracy of 85.7%, indicating good accuracy of the model. Thus, this landslide susceptibility map could help the local governments in landslide hazard mitigation, land use planning, and landscape protection.