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

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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THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA

Geoplanning Journal of Geomatics and Planning ◽

10.14710/geoplanning.4.2.157-170 ◽

2017 ◽

Vol 4 (2) ◽

pp. 157 ◽

Cited By ~ 2

Author(s):

Andang Suryana Soma ◽

Tetsuya Kubota

Keyword(s):

Land Use ◽

Land Use Change ◽

Landslide Susceptibility ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Causative Factors ◽

Predictive Rate

The study aims to develop and apply land use change (LUC) performance on landslide susceptibility map using frequency ratio (FR), and Logistic regression (LR) method in a geographic information system. In the study area, Upper Ujung-loe Watersheds area of Indonesia, landslides were detected using field survey and air photography from time series data image of Google Earth Pro from 2012 to 2016 and LUC from 2004 to 2011. Landslide susceptibility map (LSM) was constructed using FR and LR with nine causative factors. The result indicated that LUC affect the production of LSM. Validation of landslide susceptibility was carried out in this study at both with and without LUC causative factors. First, performances of each landslide model were tested using AUC curve for success and predictive rate. The highest value of predictive rate at with LUC in both FR and LR method were 83.4 % and 85.2 %, respectively. In the second stage, the ratio of landslides falling on high to a very high class of susceptibility was obtained, which indicates the level of accuracy of the method.LR method with LUC had the highest accuracy of 80.24 %. Taken together, the results suggested that changing the vegetation to another landscape causes slopes unstable and increases probability to landslide occurrence.

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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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Weights of Evidence Modeling for Landslide Susceptibility Mapping of Kabi-Gebro Locality, Gundomeskel area, Central Ethiopia

10.21203/rs.3.rs-45713/v1 ◽

2020 ◽

Author(s):

Nega Getachew ◽

Matebie Meten

Keyword(s):

Landslide Susceptibility ◽

Agricultural Land ◽

Google Earth ◽

Weights Of Evidence ◽

Susceptibility Map ◽

Landslide Susceptibility Map ◽

Data Set ◽

Causative Factors ◽

Central Highland ◽

The Impact

Abstract Kabi-Gebro area is located within the Abay Basin at Dera District of North Shewa Zone near Gundomeskel town in the Central highland of Ethiopia and it is about 320 Km from Addis Ababa. This is characterized by undulating topography, intense rainfall, active erosion and highly cultivated area. Geologically characterized by weathered sedimentary and volcanic rocks. Currently, landslides are creating serious challenges in road construction, farming practices and affecting people in this area. Active landslides in this area damaged the gravel road, houses and agricultural land. The main objective of this research is to prepare the landslide susceptibility map. To overcome the landslide problem in this area, landslide susceptibility map was prepared using GIS- based Weights of Evidence model. Based on detailed field assessment and Google Earth image interpretation, 514 landslide locations were identified and classified randomly as training landslide (80%) and validation landslide (20%). The training landslide data set include nine landslide causative factors such as lithology, slope angle, aspect, curvature, land use/land cover, distance to stream, distance to lineament, distance to spring and rainfall inorder to prepare landslide susceptibility map in this study. The landslide susceptibility maps were prepared by adding the weights of contrast values of the nine causative factors using rater calculator in the spatial analyst tool of ArcGIS. The final landslide susceptibility map was reclassified as very low, low, moderate, high and very high landslide susceptiblity classes. This susceptibility map was validated using landslide density index and Area Under the Curve (AUC). The result from this validation showed a success rate and avalidaton rate accuracies of 82.4% and 83.4% respectively for this model. Finally, this study recommends application of appropriate mitigation or corrective measures in order to lessen the impact of landslide in the area.

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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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Evaluation of Underlying Causative Factors for Earthquake-Induced Landslides and Landslide Susceptibility Mapping in Upper Indrawati Watershed, Nepal

10.21203/rs.3.rs-72223/v1 ◽

2020 ◽

Author(s):

Pawan Gautam ◽

Tetsuya Kubota ◽

Aril Aditian

Keyword(s):

Landslide Susceptibility ◽

Slope Angle ◽

Seismic Intensity ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Susceptibility Map ◽

Area Index ◽

Landslide Occurrence ◽

Causative Factors ◽

Natural Break

Abstract The main objectives of this study are to assess the underlying causative factors for landslide occurrence due to earthquake in upper Indrawati Watershed of Nepal and evaluating the region prone to landslide using Landslide Susceptibility Mapping (LSM). We used logistic regression (LR) for LSM on geographic information system (GIS) platform. Nine causal factors (CF) including slope angle, aspect, elevation, curvature, distance to fault and river, geological formation, seismic intensity, and land cover were considered for LSM. We assessed the distribution of landslide among the classes of each CF to understand the relationship of CF and landslides. The northern part of the study area, which is dominated by steep rocky slope have a higher distribution of earthquake-induced landslides. Among the CF, 'slope' showed the positive correlation as landslide distribution is increasing with increasing slope. However, LR analysis depict 'distance to the fault' is the best predictor with the highest coefficient value. Susceptibility map was validated by assessing the correctly classified landslides under susceptibility categories, generated in five discrete classes using natural break (Jenk) methods. Calculation of area under curve (AUC) and seed cell area index (SCAI) were performed to validate the susceptibility map. The LSM approach shows good accuracy with respective AUC value for success rate and prediction rate of 0.795 and 0.702. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of LSM approach.

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

KURVATEK ◽

10.33579/krvtk.v1i1.222 ◽

2016 ◽

Vol 1 (1) ◽

pp. 8

Author(s):

Dwi Winarti ◽

Srijono Srijono

Keyword(s):

Hydrothermal Alteration ◽

Landslide Susceptibility ◽

Susceptibility Map ◽

Mountainous Areas ◽

Landslide Susceptibility Map ◽

Landslide Occurrence ◽

Slope Inclination ◽

Mountain West ◽

Total Study Area ◽

Lombok Island

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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ANALISIS TINGKAT KERAWANAN TANAH LONGSOR MENGGUNAKAN METODE FREKUENSI RASIO DI DAERAH ALIRAN SUNGAI BIALO

PERENNIAL ◽

10.24259/perennial.v15i1.6317 ◽

2019 ◽

Vol 15 (1) ◽

pp. 42

Author(s):

Nurul Fadilah ◽

Usman Arsyad ◽

Andang Suryana Soma

Keyword(s):

Landslide Susceptibility ◽

Frequency Ratio ◽

High Accuracy ◽

The Other ◽

Susceptibility Map ◽

High Susceptibility ◽

Landslide Susceptibility Map ◽

Causative Factors ◽

Very High ◽

Slope Direction

Landslide is a movement of soil with slope direction and moves it on a slide. This study aimed to predict the landslide susceptibility map by using a frequency ratio. It used seven causative factors, such as slope, curvature, land use, lithology, distance to a river, distance to lineament, and rainfall. The result showed the AUC of success rate and predicted rate produced high accuracy with 0.907 and 0.904, respectively. According to the frequency ratio, the slope was the most influential than the other causative factors with 7.15. The landslide susceptibility divided into five classes, i.e. very low, low, moderate, high, and very high. Landslide susceptibility with very high and high was 19%. Moreover, classes susceptibility of very low, low, and moderate were 71%. The presentation of very high and high susceptibility is low, but it was located on an upper stream, and it will be a danger if to the downstream.

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Analysis of the landslide susceptibility map using frequency ratio method in sub-sub-Watershed Mamasa

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/886/1/012088 ◽

2021 ◽

Vol 886 (1) ◽

pp. 012088

Author(s):

Rizki Amaliah ◽

Andang Suryana Soma ◽

Baharruddin Mappangaja ◽

Friska Mambela

Keyword(s):

Success Rate ◽

Landslide Susceptibility ◽

Frequency Ratio ◽

Causative Factor ◽

Hydropower Plant ◽

Ratio Method ◽

Susceptibility Map ◽

Contributing Factors ◽

Landslide Susceptibility Map ◽

Frequency Ratio Method

Abstract Landslides that often occur in the Subs watershed of Mamasa increase the sedimentation rate so that the Bakaru hydropower plant becomes less than optimal. The contributing factors to lanslide susceptibility are land closure, lithology, curve, slope direction aspect, slope, precipitation, fault distance, and river distance. The research aims to determine the most influential erosion causative factor in Mamasa Sub-watershed by building a landslide susceptibility map using the frequency ratio method. The most significant factor is land closure, with a value of 2.03, indicating a high probability of lanslide events. The model’s success rate and prediction rate’s success rate were expressed fairly well with 0.754 and 0.752. Based on the insanity map, the Region is very high and high at 23.74% and 12.52%; insanity is moderate, low, and very low consecutively at 27.44 %, 23.77, and 12.33%.

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