scholarly journals DEVELOPING OF TRIGRS (TRANSIENT RAINFALL INFILTRATION AND GRID-BASED REGIONAL SLOPE–STABILITY ANALYSIS) INTO TRIGRS MAP FOR LANDSLIDE SUSCEPTIBILITY MAPPING

GEOMATIKA ◽  
2016 ◽  
Vol 22 (1) ◽  
pp. 37 ◽  
Author(s):  
Yunarto Yunarto
Keyword(s):  
Slope Stability ◽  
Landslide Susceptibility ◽  
Pore Water Pressure ◽  
Water Pressure ◽  
Landslide Susceptibility Mapping ◽  
Mapping Technique ◽  
Susceptibility Map ◽  
Process Data ◽  
Landslide Susceptibility Map ◽  
Pressure Changes

<p class="judulabstrakindo">ABSTRACT</p><p>TRIGRS is a modeling program for slope stability against the occurrence of landslide and pore water pressure changes due to infiltration of rainfall. There are two problems in TRIGRS operation. Firstly, whole data must have no mistake before being executed in TRIGRS. Secondly, TRIGRS is not completed by spatial visualization based on Geographic Information System (GIS), so it needs GIS <em>e.g.</em> MapInfo, ArcGIS, and ILWIS to visualize its result. The purpose of this paper is present the result of development of TRIGRS MAP by using integrated mapping technique between MapInfo and Visual Basic. Implementation of TRIGRS MAP for Bandung Regency area has generated a landslide susceptibility map of the area. By using TRIGRS MAP, user can avoid a mistake in data initialization and directly visualize its result as a map. Thus, TRIGRS MAP can be used to process data from other area for creating the landslide susceptibility map more easily and efficiently</p><p> </p>

scholarly journals Slope Stability Assessment and Landslide Susceptibility Map Production of Wadi Dhahr Area, Northwest of Sana’a, Yemen

2018 ◽  
Vol 23 (2) ◽  
pp. 120
Author(s):  
Barahim Adnan A. ◽  
Khanbari Khaled M. ◽  
Algodami Amal F. ◽  
Almadhaji Ziad A. ◽  
Adris Ahmed M.
Keyword(s):  
Slope Stability ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Satellite Image ◽  
Slope Angle ◽  
Field Investigation ◽  
Susceptibility Map ◽  
Stability Assessment ◽  
Landslide Susceptibility Map ◽  
Modes Of Failure

A slope stability assessment of Wadi Dhahr area, located northwest of Sana’a the capital of Yemen, was carried out in this study. The study area consists of sandstone and volcanic rocks that are deformed by number of faults, joints and basaltic dykes. All the important factors affecting slope stability in the area such as slope angle, slope height, discontinuities measurements, weathering, vegetation cover, rainfall and previous landslides were evaluated. The study was conducted based on the integration of field investigation and satellite image processing. A landslide susceptibility map was produced with the Landslide Possibility Index (LP1) System, and the correlation values were computed between the factors measured and Landslide Possibility Index values. The fractures counted by satellite image were categorised according to their length and zones based on their concentrations. It was found that plain sliding and rockfall are the main modes of failure in the area, while rolling and toppling are rare. Some remedial measures are proposed to protect the slopes where it is needed,  such as the removal of rock overhangs, unstable blocks and trees, and by supporting the toe of slopes and overhanging parts by retaining walls and erecting well sealed drainage conduits. The results will assist in slope management and land use planning in the area.

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

2012 ◽  
Vol 225 ◽  
pp. 442-447 ◽  
Author(s):  
Biswajeet Pradhan ◽  
Zulkiflee Abd. Latif ◽  
Siti Nur Afiqah Aman
Keyword(s):  
High Resolution ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Susceptibility Mapping ◽  
Airborne Lidar ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Ratio Model ◽  
Landslide Susceptibility Map ◽  
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.

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

2020 ◽  
Vol 8 (6) ◽  
pp. 398-404
Keyword(s):  
High Risk ◽  
Landslide Susceptibility ◽  
Catchment Area ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Hazard Zonation ◽  
Certainty Factor ◽  
Landslide Susceptibility Map ◽  
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.

scholarly journals 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

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.

The Effect of Rainfall Infiltration Time to the Unsaturated Soil Slope Stability

2011 ◽  
Vol 71-78 ◽  
pp. 4864-4867
Author(s):  
Guang Hua Cai ◽  
Hai Jun Lu ◽  
Wei He ◽  
Long Guan ◽  
Wei Qi Xu
Keyword(s):  
Slope Stability ◽  
Pore Water ◽  
Pore Water Pressure ◽  
Limit Equilibrium ◽  
Water Pressure ◽  
Rainfall Infiltration ◽  
Soil Slope ◽  
Soil Permeability ◽  
Rainfall Condition ◽  
Pressure Changes

Rainfall infiltration is currently one of the important factors in studying the soil-slope stability. By using saturated-unsaturated seepage theory, the traditional limit equilibrium method and so on, analyze the water content and the pore-water pressure changes under the rainfall condition, then analyze the influence mechanism of the slope stability. Through the Seep/W and the Slope/W of the GEO-Slope software, do the numerical simulation of the slope stability under the rainfall condition, to seek the distribution of pore-water pressure on the rainfall situation and the influence of the seepage field from various parameters such as rainfall intensity and the soil permeability coefficient, thus to study the slope stability.

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

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.

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

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):  
Landslide Susceptibility ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
High Susceptibility ◽  
Landslide Susceptibility Map ◽  
Landslide Occurrence ◽  
Mountainous Regions ◽  
Geological Conditions ◽  
Very High

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.

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

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1047 ◽  
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.

scholarly journals Landslide Susceptibility Mapping in Darjeeling Himalayas, India

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Amit Chawla ◽  
Sowmiya Chawla ◽  
Srinivas Pasupuleti ◽  
A. C. S. Rao ◽  
Kripamoy Sarkar ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Landslide Susceptibility ◽  
Remote Sensing Data ◽  
Geographic Information ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Swarm Optimization

Landslide susceptibility map aids decision makers and planners for the prevention and mitigation of landslide hazard. This study presents a methodology for the generation of landslide susceptibility mapping using remote sensing data and Geographic Information System technique for the part of the Darjeeling district, Eastern Himalaya, in India. Topographic, earthquake, and remote sensing data and published geology, soil, and rainfall maps were collected and processed using Geographic Information System. Landslide influencing factors in the study area are drainage, lineament, slope, rainfall, earthquake, lithology, land use/land cover, fault, valley, soil, relief, and aspect. These factors were evaluated for the generation of thematic data layers. Numerical weight and rating for each factor was assigned using the overlay analysis method for the generation of landslide susceptibility map in the Geographic Information System environment. The resulting landslide susceptibility zonation map demarcated the study area into four different susceptibility classes: very high, high, moderate, and low. Particle Swarm Optimization-Support Vector Machine technique was used for the prediction and classification of landslide susceptibility classes, and Genetic Programming method was used to generate models and to predict landslide susceptibility classes in conjunction with Geographic Information System output, respectively. Genetic Programming and Particle Swarm Optimization-Support Vector Machine have performed well with respect to overall prediction accuracy and validated the landslide susceptibility model generated in the Geographic Information System environment. The efficiency of the landslide susceptibility zonation map was also confirmed by correlating the landslide frequency between different susceptible classes.

scholarly journals LANDSLIDE SUSCEPTIBILITY MAPPING WITH RANDOM FOREST MODEL FOR ORDU, TURKEY

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1229-1236 ◽  
Author(s):  
G. Karakas ◽  
R. Can ◽  
S. Kocaman ◽  
H. A. Nefeslioglu ◽  
C. Gokceoglu
Keyword(s):  
Random Forest ◽  
Landslide Susceptibility ◽  
Digital Terrain Model ◽  
Disaster Mitigation ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Effective Parameters ◽  
Landslide Susceptibility Map ◽  
Random Forest Method ◽  
Susceptibility Maps

Abstract. Landslides are among commonly observed natural hazards all over the world and can be quite destructive for infrastructure and in settlement areas. Their occurrences are often related with extreme meteorological events and seismic activities. Preparation of landslide susceptibility maps is important for disaster mitigation efforts and to increase the resilience. The factors effective on landslide susceptibility map production depend mainly on the topography, land use and the geological characteristics of the region. The up-to-date and accurate data needed for extracting the effective parameters can be obtained by using photogrammetric techniques with high spatial resolution. Data driven ensemble methods are being increasingly used for landslide susceptibility map production and accurate results can be obtained. In this study, regional landslide susceptibility map of a landslide-prone area in a part of Ordu Province in northern Turkey is produced using topographic and lithological parameters by employing the random forest method. An actual landslide inventory delineated manually by geologists using the produced orthophotos and the digital terrain model (DTM) is used for training the model. The results show that an accuracy of 83% and precision of 92% can obtained from the data and the random forest method. The approach can be applied for generation of regional susceptibility maps semi-automatically.

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