PROCESS IN THE HYDROTHERMALLY ALTERED AREA AT SOUTHERN MOUNTAIN OF LOMBOK ISLAND, INDONESIA

Landslide disasters are abundant in the mountainous areas of Lombok Island, Indonesia. Most landslides frequently occur in areas intensively suffered by hydrothermal alteration including Pelangan Village at Southern Mountain, West Lombok Regency. The objective of this study are to identify the most important factors controlling landslide and also to analyze the landslide susceptibility zones in the hydrothermally altered area. For this purpose, it is necessary to prepare the landslide investigation and landslide susceptibility map. In this study, the AnalyticalHierarchyProcess (AHP) is used to develop landslide susceptibility map. The landslide susceptibility was analyzed by applying weighting and scoring on each factor controlling the landslide occurrence, such as hydrothermal alteration, slope inclination, distance to lineament, and landuse.The result shows that hydrothermal alteration and slope inclination are the most important parameters to landslide occurence (39.35%), and the least important factor are distance to lineament (13.76%), and landuse (7.54%). The high susceptible zones (HS) cover about 34.20% of the total study area. The moderate susceptible zones (MS) cover about 18.40% of the total area, while about 27.80% of the total study area were classified as being the low susceptible zone (LS), and about 19.60% of the total study area are classified as very low susceptible zone (VLS).

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Landslide susceptibility map using certainty factor for hazard mitigation in mountainous areas of Ujung-loe watershed in South Sulawesi

Forest and Society ◽

10.24259/fs.v2i1.3594 ◽

2018 ◽

Vol 2 (1) ◽

pp. 79 ◽

Cited By ~ 2

Author(s):

Andang Suryana Soma ◽

Tetsuya Kubota

Keyword(s):

Landslide Susceptibility ◽

Causative Factor ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Sparse Vegetation ◽

Dense Vegetation ◽

South Sulawesi ◽

Landslide Hazards ◽

Causative Factors

This study aims to build a landslide susceptibility map (LSM) by using certainty factor (CF) models for mitigation of landslide hazards and mitigation for people who live near to the forest. In the study area, the mountainous area of the Ujung-loe watersheds of South Sulawesi, Indonesia, information on landslides were derived from aerial photography using time series data images from Google Earth Pro© from 2012 to 2016 and field surveys. The LSM was built by using a CF model with eleven causative factors. The results indicated that the causative factor with the highest impact on the probability of landslide occurrence is the class of change from dense vegetation to sparse vegetation (4-1), with CF value 0.95. The CF method proved to be an excellent method for producing a landslide susceptibility map for mitigation with an area under curve (AUC) success rate of 0.831, and AUC predictive rate 0.830 and 85.28% of landslides validation fell into the high to very high class. In conclusion, correlations between landslide occurrence with causative factors shows an overall highest LUC causative factor related to the class of change from dense vegetation to sparse vegetation, resulting in the highest probability of landslide occurrence. Thus, forest areas uses at these locations should prioritize maintaining dense vegetation and involving the community in protection measures to reduce the occurrence of landslide risk. LSM models that apply certainty factors can serve as guidelines for mitigation of people living in this area to pay attention to landslide hazards with high and very high landslide vulnerability and to be careful to avoid productive activities at those locations.

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The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

Open Geosciences ◽

10.1515/geo-2016-0010 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 9

Author(s):

Lee Saro ◽

Jeon Seong Woo ◽

Oh Kwan-Young ◽

Lee Moung-Jin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Logistic Regression ◽

Landslide Susceptibility ◽

Regression Models ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Logistic Regression Models ◽

Artificial Neural

AbstractThe aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330), and ‘aspect’ yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

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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):

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.

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Analysis of Landslide Susceptibility Using Deep Neural Network

Korean Society of Hazard Mitigation ◽

10.9798/kosham.2021.21.3.141 ◽

2021 ◽

Vol 21 (3) ◽

pp. 141-150

Author(s):

Chang-Ho Song ◽

Ji-Sung Lee ◽

Yun-Tae Kim

Keyword(s):

Neural Network ◽

Success Rate ◽

Landslide Susceptibility ◽

Deep Neural Network ◽

Human Life ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Conditioning Factors ◽

Prediction Rate

Landslides in Korea are caused by various factors, such as topographic characteristics, geology, and climate change, and they cause significant damage to property and human life. It is necessary to analyze landslide susceptibility to identify the location of landslide occurrence precisely and respond to the risk of landslides. In this study, the probability of landslide occurrence was calculated through a landslide sensitivity analysis using a deep neural network based on eight conditioning factors and 26 landslide data. In addition, verification was performed using the ROC method. The landslide susceptibility obtained using a deep neural network showed a success rate of 70% and a prediction rate of 81.7%, indicating that the prediction rate was 11.7% higher than the success rate. In addition, a landslide susceptibility map for estimating the probability of landslide occurrence was plotted using the geometric spacing method. The chi-square test results indicated that the landslide susceptibility map obtained in this study was statistically significant. The location of landslides can be identified more accurately using the proposed method.

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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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