scholarly journals Comparison of Logistic Regression, Information Value, and Comprehensive Evaluating Model for Landslide Susceptibility Mapping

2021 ◽  
Vol 13 (7) ◽  
pp. 3803
Author(s):  
Rui-Xuan Tang ◽  
E-Chuan Yan ◽  
Tao Wen ◽  
Xiao-Meng Yin ◽  
Wei Tang
Keyword(s):  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Information Value ◽  
Landslide Susceptibility Mapping ◽  
Slope Length ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Slope Structure

This study validated the robust performances of the recently proposed comprehensive landslide susceptibility index model (CLSI) for landslide susceptibility mapping (LSM) by comparing it to the logistic regression (LR) and the analytical hierarchy process information value (AHPIV) model. Zhushan County in China, with 373 landslides identified, was used as the study area. Eight conditioning factors (lithology, slope structure, slope angle, altitude, distance to river, stream power index, slope length, distance to road) were acquired from digital elevation models (DEMs), field survey, remote sensing imagery, and government documentary data. Results indicate that the CLSI model has the highest accuracy and the best classification ability, although all three models can produce reasonable landslide susceptibility (LS) maps. The robust performance of the CLSI model is due to its weight determination by a back-propagation neural network (BPNN), which successfully captures the nonlinear relationship between landslide occurrence and the conditioning factors.

scholarly journals Landslide Susceptibility Mapping for the Muchuan County (China): A Comparison Between Bivariate Statistical Models (WoE, EBF, and IoE) and Their Ensembles with Logistic Regression

Symmetry ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 762 ◽  
Author(s):  
Renwei Li ◽  
Nianqin Wang
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Aerial Photographs ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Topographic Wetness Index

The main purpose of this study is to apply three bivariate statistical models, namely weight of evidence (WoE), evidence belief function (EBF) and index of entropy (IoE), and their ensembles with logistic regression (LR) for landslide susceptibility mapping in Muchuan County, China. First, a landslide inventory map contained 279 landslides was obtained through the field investigation and interpretation of aerial photographs. Next, the landslides were randomly divided into two parts for training and validation with the ratio of 70/30. In addition, according to the regional geological environment characteristics, twelve landslide conditioning factors were selected, including altitude, plan curvature, profile curvature, slope angle, distance to roads, distance to rivers, topographic wetness index (TWI), normalized different vegetation index (NDVI), land use, soil, and lithology. Subsequently, the landslide susceptibility mapping was carried out by the above models. Eventually, the accuracy of this research was validated by the area under the receiver operating characteristic (ROC) curve and the results indicated that the landslide susceptibility map produced by EBF-LR model has the highest accuracy (0.826), followed by IoE-LR model (0.825), WoE-LR model (0.792), EBF model (0.791), IoE model (0.778), and WoE model (0.753). The results of this study can provide references of landslide prevention and land use planning for local government.

Landslide Susceptibility Mapping and Model Performance Assessment

2013 ◽  
Vol 353-356 ◽  
pp. 3487-3493 ◽  
Author(s):  
Chen Chao Xiao ◽  
Yuan Tian ◽  
Kang Ping Si ◽  
Ting Li
Keyword(s):  
Logistic Regression ◽  
Performance Assessment ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Model Performance ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Maps ◽  
Overall Performance ◽  
Better Than

In this paper landslide susceptibility mapping and model performance assessment was conducted using three models, logistic regression, GAM, and SVM, in a study area in Shenzhen, China. Ten factors, slope angle, aspect, elevation, plan and profile curvature of the slope, lithology, NDVI, building density, the distance to the river, and the distance to the fault were selected as influencing factors for the landslide occurrences. All three models were trained and the resulting susceptibility maps were created. The performances of the three models were then assessed by AUC values through a 10-fold cross-validation. It could be concluded that in the study area GAM had the best overall performance among the three models, while SVM was better than logistic regression. Based on the derived DPR values, the optimum thresholds between stable areas and risky areas for all three models were also determined.

scholarly journals Landslide Susceptibility Mapping using Bivariate Statistical Models and GIS in Chattagram District, Bangladesh

2021 ◽  
Author(s):  
Md. Sharafat Chowdhury ◽  
Bibi Hafsa
Keyword(s):  
Statistical Models ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Information Value ◽  
Weight Of Evidence ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Topographic Wetness Index ◽  
Conditioning Factors

Abstract This study attempts to produce Landslide Susceptibility Map for Chattagram District of Bangladesh by using five GIS based bivariate statistical models, namely the Frequency Ratio (FR), Shanon’s Entropy (SE), Weight of Evidence (WofE), Information Value (IV) and Certainty Factor (CF). A secondary landslide inventory database was used to correlate the previous landslides with the landslide conditioning factors. Sixteen landslide conditioning factors of Slope Aspect, Slope Angle, Geology, Elevation, Plan Curvature, Profile Curvature, General Curvature, Topographic Wetness Index, Stream Power Index, Sediment Transport Index, Topographic Roughness Index, Distance to Stream, Distance to Anticline, Distance to Fault, Distance to Road and NDVI were used. The Area Under Curve (AUC) was used for validation of the LSMs. The predictive rate of AUC for FR, SE, WofE, IV and CF were 76.11%, 70.11%, 78.93%, 76.57% and 80.43% respectively. CF model indicates 15.04% of areas are highly susceptible to landslide. All the models showed that the high elevated areas are more susceptible to landslide where the low-lying river basin areas have a low probability of landslide occurrence. The findings of this research will contribute to land use planning, management and hazard mitigation of the CHT region.

scholarly journals SPATIAL RESOLUTION EFFECTS OF DIGITAL TERRAIN MODELS ON LANDSLIDE SUSCEPTIBILITY ANALYSIS

2016 ◽  
Vol XLI-B8 ◽  
pp. 33-36 ◽  
Author(s):  
K. T. Chang ◽  
J. Dou ◽  
Y. Chang ◽  
C. P. Kuo ◽  
K. M. Xu ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Total Curvature ◽  
Slope Angle ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Landslide Occurrence ◽  
Conditioning Factors ◽  
Digital Elevation ◽  
Artificial Neural Network Ann ◽  
Elevation Model

The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.

Using landslide-inventory mapping for a combined bagged-trees and logistic-regression approach to determining landslide susceptibility in eastern Kentucky, United States

2021 ◽  
pp. qjegh2020-177
Author(s):  
Matthew M. Crawford ◽  
Jason M. Dortch ◽  
Hudson J. Koch ◽  
Ashton A. Killen ◽  
Junfeng Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Standard Deviation ◽  
Landslide Susceptibility ◽  
Engineering Geology ◽  
Susceptibility Mapping ◽  
Landslide Inventory ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Occurrence

High-resolution LiDAR-derived datasets from a 1.5-m digital elevation model and a detailed landslide inventory (N ≥ 1,000) for Magoffin County, Kentucky, USA, were used to develop a combined machine-learning and statistical approach to improve geomorphic-based landslide-susceptibility mapping.An initial dataset of 36 variables was compiled to investigate the connection between slope morphology and landslide occurrence. Bagged trees, a machine-learning random-forest classifier, was used to evaluate the geomorphic variables, and 12 were identified as important: standard deviation of plan curvature, standard deviation of elevation, sum of plan curvature, minimum slope, mean plan curvature, range of elevation, sum of roughness, mean curvature, sum of curvature, mean roughness, minimum curvature, and standard deviation of curvature. These variables were further evaluated using logistic regression to determine the probability of landslide occurrence and then used to create a landslide-susceptibility map.The performance of the logistic-regression model was evaluated by the receiver operating characteristic curve, area under the curve, which was 0.83. Standard deviations from the probability mean were used to set landslide-susceptibility classifications: low (0–0.10), low–moderate (0.11–0.27), moderate (0.28–0.44), moderate–high (0.45–0.7), and high (0.7–1.0). Logistic-regression results were validated by using a separate landslide inventory for the neighboring Prestonsburg 7.5-minute quadrangle, and running the same regression function. Results indicate that 74.9 percent of the landslide deposits were identified as having moderate, moderate–high, or high landslide susceptibility. Combining inventory mapping with statistical modelling identified important geomorphic variables and produced a useful approach to landslide-susceptibility mapping.Thematic collection: This article is part of the Digitization and Digitalization in engineering geology and hydrogeology collection available at: https://www.lyellcollection.org/cc/digitization-and-digitalization-in-engineering-geology-and-hydrogeology

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

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.

scholarly journals Remote Sensing and GIS Approach for Landslide Susceptibility Mapping: A Case Study in Hofu City, Yamaguchi, Japan

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Benita Nathania ◽  
Fusanori Muira
Keyword(s):  
Remote Sensing ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Slope Angle ◽  
Remote Sensing Data ◽  
Bare Soil ◽  
Landslide Susceptibility Mapping ◽  
Susceptibility Map ◽  
Landslide Occurrence ◽  
Landslide Event

Landslide is one of the natural hazards that often initiates by the interaction between environmental factors and triggering factor. The identi?cation of areas where landslides are likely to occur is important for the reduction of potential damage. This study utilizes remote sensing data and Geographic Information System (GIS) to identify areas where landslides are likely to occur and generates landslide susceptibility map based on logistic regression model. The study area is located in Hofu city, Yamaguchi prefecture, Japan. The data that were used in this study are satellite imagery from ALOS AVNIR-2, elevation and geology data from GSI, Rainfall data from AMEDAS, and landslide inventory map provided from Ministry of Land, Infrastructure, Transportation and Tourism. The result from this study revealed that elevation from > 50 to < 350 m, slope angle from> 5° to < 50°, slope direction of north and northeast, land cover of agriculture, urban, bare soil, and forest, and lithology of graniodorite, fan deposits, and middle terrace are favorable for landslide occurrence. The landslide susceptiility map showed that 98% of the result calculations of logistic regression are similar to the historical data of landslide event which is among 911 landslide points, 899 points were existed in high and very high susceptibility areas.

Sign in / Sign up

Export Citation Format

Share Document