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 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.

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 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 of SE Serbia using GIS

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.

scholarly journals A Novel Hybrid Method for Landslide Susceptibility Mapping-Based GeoDetector and Machine Learning Cluster: A Case of Xiaojin County, China

2021 ◽  
Vol 10 (2) ◽  
pp. 93
Author(s):  
Wei Xie ◽  
Xiaoshuang Li ◽  
Wenbin Jian ◽  
Yang Yang ◽  
Hongwei Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Method ◽  
Roc Curve ◽  
Landslide Susceptibility ◽  
Susceptibility Mapping ◽  
Assessment Model ◽  
Landslide Susceptibility Mapping ◽  
Support Vector ◽  
Susceptibility Map ◽  
Area Index

Landslide susceptibility mapping (LSM) could be an effective way to prevent landslide hazards and mitigate losses. The choice of conditional factors is crucial to the results of LSM, and the selection of models also plays an important role. In this study, a hybrid method including GeoDetector and machine learning cluster was developed to provide a new perspective on how to address these two issues. We defined redundant factors by quantitatively analyzing the single impact and interactive impact of the factors, which was analyzed by GeoDetector, the effect of this step was examined using mean absolute error (MAE). The machine learning cluster contains four models (artificial neural network (ANN), Bayesian network (BN), logistic regression (LR), and support vector machines (SVM)) and automatically selects the best one for generating LSM. The receiver operating characteristic (ROC) curve, prediction accuracy, and the seed cell area index (SCAI) methods were used to evaluate these methods. The results show that the SVM model had the best performance in the machine learning cluster with the area under the ROC curve of 0.928 and with an accuracy of 83.86%. Therefore, SVM was chosen as the assessment model to map the landslide susceptibility of the study area. The landslide susceptibility map demonstrated fit with landslide inventory, indicated the hybrid method is effective in screening landslide influences and assessing landslide susceptibility.

scholarly journals Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (11) ◽  
pp. 2166
Author(s):  
Xin Yang ◽  
Rui Liu ◽  
Mei Yang ◽  
Jingjue Chen ◽  
Tianqiang Liu ◽  
...  
Keyword(s):  
Land Use ◽  
Hybrid Model ◽  
Landslide Susceptibility ◽  
Spatial Information ◽  
Landslide Susceptibility Mapping ◽  
Ratio Method ◽  
Support Vector ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Proposed Model

This study proposed a new hybrid model based on the convolutional neural network (CNN) for making effective use of historical datasets and producing a reliable landslide susceptibility map. The proposed model consists of two parts; one is the extraction of landslide spatial information using two-dimensional CNN and pixel windows, and the other is to capture the correlated features among the conditioning factors using one-dimensional convolutional operations. To evaluate the validity of the proposed model, two pure CNN models and the previously used methods of random forest and a support vector machine were selected as the benchmark models. A total of 621 earthquake-triggered landslides in Ludian County, China and 14 conditioning factors derived from the topography, geological, hydrological, geophysical, land use and land cover data were used to generate a geospatial dataset. The conditioning factors were then selected and analyzed by a multicollinearity analysis and the frequency ratio method. Finally, the trained model calculated the landslide probability of each pixel in the study area and produced the resultant susceptibility map. The results indicated that the hybrid model benefitted from the features extraction capability of the CNN and achieved high-performance results in terms of the area under the receiver operating characteristic curve (AUC) and statistical indices. Moreover, the proposed model had 6.2% and 3.7% more improvement than the two pure CNN models in terms of the AUC, respectively. Therefore, the proposed model is capable of accurately mapping landslide susceptibility and providing a promising method for hazard mitigation and land use planning. Additionally, it is recommended to be applied to other areas of the world.

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

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.

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.

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