scholarly journals Landslide Identification and Zonation Using the Index of Entropy Technique at Ossey Watershed Area in Bhutan

2020 ◽  
pp. 102-115
Author(s):  
Thongley Thongley ◽  
Chaiwiwat Vansarochana
Keyword(s):  
Landslide Susceptibility ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Kappa Index ◽  
Landslide Susceptibility Map ◽  
Causal Factors ◽  
Watershed Area ◽  
Index Of Entropy

The landslide is one of the natural disasters which claim human lives and incur huge economic losses, especially in the mountainous area. The main aim of this study is to develop different zones of landslide-prone area using the index of entropy (IOE) at the Ossey watershed area in Bhutan. During the landslide inventory, 164 landslides were identified of which 115 locations were used for the training dataset while the remaining 49 locations were used for the validation dataset. A total of ten causal factors were used for this study including elevation, slope, aspect, slope curvature, stream power index, normalized difference vegetation index (NDVI), distance from the road, distance from the river, lithology, and rainfall. The IOE was used to obtain the relationship between the landslide events and the causal factors. The most influential causal factors were NDVI, slope, and rainfall with the weightage of 0.377, 0.347, and 0.175 respectively as per the IOE. The final landslide susceptibility map was classified into five classes using the geometrical interval classification. The validation was done using the receiver operating characteristic (ROC) curves and the kappa index. The area under the curve (AUC) for the success rate and prediction rate was 0.7821 and 0.8377, respectively. The kappa index using the training dataset and validation dataset were 0.4111 and 0.4898, respectively. The final landslide susceptibility map is accurate enough for the future references by the decision-makers and the engineers.

Production of Landslide Susceptibility Map using Bayesian Probability Model

2015 ◽  
Vol 4 (2) ◽  
pp. 16-33 ◽  
Author(s):  
Halil Akıncı ◽  
Ayşe Yavuz Özalp ◽  
Mehmet Özalp ◽  
Sebahat Temuçin Kılıçer ◽  
Cem Kılıçoğlu ◽  
...  
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Probability Model ◽  
Bayesian Probability ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Landslide Susceptibility Map

Artvin is one of the provinces in Turkey where landslides occur most frequently. There have been numerous landslides characterized as natural disaster recorded across the province. The areas sensitive to landslides across the province should be identified in order to ensure people's safety, to take the necessary measures for reducing any devastating effects of landslides and to make the right decisions in respect to land use planning. In this study, the landslide susceptibility map of the Central district of Artvin was produced by using Bayesian probability model. Parameters including lithology, altitude, slope, aspect, plan and profile curvatures, soil depth, topographic wetness index, land cover, and proximity to the road and stream were used in landslide susceptibility analysis. The landslide susceptibility map produced in this study was validated using the receiver operating characteristics (ROC) based on area under curve (AUC) analysis. In addition, control landslide locations were used to validate the results of the landslide susceptibility map and the validation analysis resulted in 94.30% accuracy, a reliable outcome for this map that can be useful for general land use planning in Artvin.

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

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

scholarly journals Hybrid Computational Intelligence Methods for Landslide Susceptibility Mapping

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 325 ◽  
Author(s):  
Guirong Wang ◽  
Xinxiang Lei ◽  
Wei Chen ◽  
Himan Shahabi ◽  
Ataollah Shirzadi
Keyword(s):  
Landslide Susceptibility ◽  
Radial Basis Function Network ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance.

scholarly journals Planet Image-Based Inventorying and Machine Learning-Based Susceptibility Mapping for the Landslides Triggered by the 2018 Mw6.6 Tomakomai, Japan Earthquake

2019 ◽  
Vol 11 (8) ◽  
pp. 978 ◽  
Author(s):  
Xiaoyi Shao ◽  
Siyuan Ma ◽  
Chong Xu ◽  
Pengfei Zhang ◽  
Boyu Wen ◽  
...  
Keyword(s):  
Success Rate ◽  
Landslide Susceptibility ◽  
Slope Failure ◽  
Slope Angle ◽  
Training Dataset ◽  
Support Vector ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Test Dataset ◽  
Prediction Rate

The 5 September 2018 (UTC time) Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing widespread concern. We attempted to establish a detailed inventory of this slope failure and use proper methods to assess landslide susceptibility in the entire affected area. To this end we applied the logistic regression (LR) and the support vector machine (SVM) for this study. Based on high-resolution (3 m) optical satellite images (planet image) before and after the earthquake, we delineated 9295 individual landslides triggered by the earthquake, occupying an area of 30.96 km2. Ten controlling factors were selected for susceptibility analysis, including elevation, slope angle, aspect, curvature, distances to faults, distances to the epicenter, Peak ground acceleration (PGA), distance to rivers, distances to roads and lithology. Using the LR and SVM, two landslide susceptibility maps were produced for the study area. The results show that in the LR model, the success rate is 84.7% between the landslide susceptibility map and the training dataset, and the prediction rate is 83.9% shown by comparing the test dataset and the landslide susceptibility map. In the SVM model, a success rate of 90.9% exists between the susceptibility map and the test samples, and a prediction rate of 87.1% from comparison of the test dataset and the landslides susceptibility map. In comparison, the performance of the SVM is slightly better than the LR model.

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 Quantitative Assessment of Landslide Susceptibility Comparing Statistical Index, Index of Entropy, and Weights of Evidence in the Shangnan Area, China

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 868 ◽  
Author(s):  
Jie Liu ◽  
Zhao Duan
Keyword(s):  
Landslide Susceptibility ◽  
Weights Of Evidence ◽  
Susceptibility Mapping ◽  
Shaanxi Province ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Susceptibility Map ◽  
Statistical Index ◽  
Index Of Entropy ◽  
Testing Dataset

In this study, a comparative analysis of the statistical index (SI), index of entropy (IOE) and weights of evidence (WOE) models was introduced to landslide susceptibility mapping, and the performance of the three models was validated and systematically compared. As one of the most landslide-prone areas in Shaanxi Province, China, Shangnan County was selected as the study area. Firstly, a series of reports, remote sensing images and geological maps were collected, and field surveys were carried out to prepare a landslide inventory map. A total of 348 landslides were identified in study area, and they were reclassified as a training dataset (70% = 244 landslides) and testing dataset (30% = 104 landslides) by random selection. Thirteen conditioning factors were then employed. Corresponding thematic data layers and landslide susceptibility maps were generated based on ArcGIS software. Finally, the area under the curve (AUC) values were calculated for the training dataset and the testing dataset in order to validate and compare the performance of the three models. For the training dataset, the AUC plots showed that the WOE model had the highest accuracy rate of 76.05%, followed by the SI model (74.67%) and the IOE model (71.12%). In the case of the testing dataset, the prediction accuracy rates for the SI, IOE and WOE models were 73.75%, 63.89%, and 75.10%, respectively. It can be concluded that the WOE model had the best prediction capacity for landslide susceptibility mapping in Shangnan County. The landslide susceptibility map produced by the WOE model had a profound geological and engineering significance in terms of landslide hazard prevention and control in the study area and other similar areas.

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

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.

scholarly journals Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement

Land ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 995
Author(s):  
Okoli Jude Emeka ◽  
Haslinda Nahazanan ◽  
Bahareh Kalantar ◽  
Zailani Khuzaimah ◽  
Ojogbane Success Sani
Keyword(s):  
Landslide Susceptibility ◽  
Ground Cover ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Water Runoff ◽  
Operating Characteristics ◽  
Topographic Wetness Index ◽  
Landslide Susceptibility Map ◽  
Ahp Model ◽  
Slope Reinforcement

A landslide is a significant environmental hazard that results in an enormous loss of lives and properties. Studies have revealed that rainfall, soil characteristics, and human errors, such as deforestation, are the leading causes of landslides, reducing soil water infiltration and increasing the water runoff of a slope. This paper introduces vegetation establishment as a low-cost, practical measure for slope reinforcement through the ground cover and the root of the vegetation. This study reveals the level of complexity of the terrain with regards to the evaluation of high and low stability areas and has produced a landslide susceptibility map. For this purpose, 12 conditioning factors, namely slope, aspect, elevation, curvature, hill shade, stream power index (SPI), topographic wetness index (TWI), terrain roughness index (TRI), distances to roads, distance to lakes, distance to trees, and build-up, were used through the analytic hierarchy process (AHP) model to produce landslide susceptibility map. Receiver operating characteristics (ROC) was used for validation of the results. The area under the curve (AUC) values obtained from the ROC method for the AHP model was 0.865. Four seed samples, namely ryegrass, rye corn, signal grass, and couch, were hydroseeded to determine the vegetation root and ground cover’s effectiveness on stabilization and reinforcement on a high-risk susceptible 65° slope between August and December 2020. The observed monthly vegetation root of couch grass gave the most acceptable result. With a spreading and creeping vegetation ground cover characteristic, ryegrass showed the most acceptable monthly result for vegetation ground cover effectiveness. The findings suggest that the selection of couch species over other species is justified based on landslide control benefits.

scholarly journals Application Of Logistic Regression Method To Produce Landslide Susceptibility Map: A Case Study Of Tetouan Mazari, Morocco

2018 ◽  
Vol 149 ◽  
pp. 02082
Author(s):  
L. Ait Brahim ◽  
M. Elmoulat
Keyword(s):  
Logistic Regression ◽  
Land Use Planning ◽  
Landslide Susceptibility ◽  
Accurate Method ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Landslide Susceptibility Map ◽  
Logistic Regression Method ◽  
Validation Set

The main purpose of this study is to use logistic regression (RL) model to map landslide susceptibility in and around the area of Tetouan Mazari in the Northern Morocco. Parameters, such as lithology, slope gradient, slope aspect, faults, drainage lines, and hillshade, were considered. Landslide susceptibility map was produced using RL method and then compared and validated. Before the modeling and validation, the observed landslides were separated into two groups. The first group was for training, and the other group was for validation steps. The accuracy of the model was measured by fitting them to a validation set of observed landslides. For validation process, the half landslides remaining was used. The final map was classified into five classes: Very High (32%), High (40%), Medium (7%), Low (7%) and Nil (15%). According to these values logistic regression was determined to be one of the most accurate method to generate landslide susceptibility map. Last but not least, logistic regression model can be used to manage and mitigate hazards related to landslides and to aid in land-use planning for the city of Tetouan‥

scholarly journals Landslide susceptibility mapping by combining the three methods Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process in Dozain basin

2014 ◽  
Vol XL-2/W3 ◽  
pp. 267-272 ◽  
Author(s):  
E. Tazik ◽  
Z. Jahantab ◽  
M. Bakhtiari ◽  
A. Rezaei ◽  
S. Kazem Alavipanah
Keyword(s):  
Fuzzy Logic ◽  
Analytical Hierarchy Process ◽  
Landslide Susceptibility ◽  
Frequency Ratio ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Ahp Method ◽  
Landslide Susceptibility Map ◽  
Hierarchy Process ◽  
Process Method

Landslides are among the most important natural hazards that lead to modification of the environment. Therefore, studying of this phenomenon is so important in many areas. Because of the climate conditions, geologic, and geomorphologic characteristics of the region, the purpose of this study was landslide hazard assessment using Fuzzy Logic, frequency ratio and Analytical Hierarchy Process method in Dozein basin, Iran. At first, landslides occurred in Dozein basin were identified using aerial photos and field studies. The influenced landslide parameters that were used in this study including slope, aspect, elevation, lithology, precipitation, land cover, distance from fault, distance from road and distance from river were obtained from different sources and maps. Using these factors and the identified landslide, the fuzzy membership values were calculated by frequency ratio. Then to account for the importance of each of the factors in the landslide susceptibility, weights of each factor were determined based on questionnaire and AHP method. Finally, fuzzy map of each factor was multiplied to its weight that obtained using AHP method. At the end, for computing prediction accuracy, the produced map was verified by comparing to existing landslide locations. These results indicate that the <b>combining the three methods</b> Fuzzy Logic, Frequency Ratio and Analytical Hierarchy Process method are relatively good estimators of landslide susceptibility in the study area. According to landslide susceptibility map about 51% of the occurred landslide fall into the high and very high susceptibility zones of the landslide susceptibility map, but approximately 26 % of them indeed located in the low and very low susceptibility zones.

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