Landslide susceptibility mapping based on frequency ratio and logistic regression models

Solaimani, Karim; Mousavi, Seyedeh Zohreh; Kavian, Ataollah

doi:10.1007/s12517-012-0526-5

Cited by 104 publications

(34 citation statements)

References 56 publications

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“…Generally, landslides occur along road cuts due to excavation, additional hydrologic load change, and drainage during the road construction process (Gayen and Saha 2017). Similarly, distance to rivers is an important landslide conditioning factor since runoff commonly decrease slope stability and leads to landslides by eroding slopes (Solaimani et al 2013). A map showing a 200 m buffer zone around roads and rivers is shown in Figure 3e and f, respectively.…”

Section: Study Areamentioning

confidence: 99%

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Wang

Guo

et al. 2019

Geomatics, Natural Hazards and Risk

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Landslide susceptibility mapping is essential in delineating landslide prone areas in mountainous regions. The primary purpose of this study is to evaluate landslide susceptibility mapping using three methods, information value (IV), weights-of-evidence (WofE), and certainty factor (CF) in southeastern Gansu province, China. Firstly, the landslide in the study area was located mainly using aerial-photo interpretation and field surveys. A total of 529 landslides were randomly divided using a ratio of 70/30 for training and validation of these methods. Eight landslide conditioning factors including slope angle, altitude, NDVI, distance to faults, distance to roads, distance to rivers, rainfall, and lithology, were considered. Landslide susceptibility maps were produced for each of the three methods. Susceptibility maps were verified and compared using the area under the curve (AUC) method. The success rate curve demonstrated that the AUC for the IV, WofE, and CF methods was 88.02, 87.56, and 89.04%, respectively, and the prediction rate curve showed that the AUC was 86.24, 86.34, and 87.44%, respectively. Furthermore, results showed that CF method had the highest accuracy in comparison with other methods. Generally, the three methods showed reasonable accuracy in landslide susceptibility mapping. Results of this study can serve as guidelines to managers and policy makers regarding the prevention and mitigation of landslide hazards.

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Section: Study Areamentioning

confidence: 99%

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Wang

Guo

et al. 2019

Geomatics, Natural Hazards and Risk

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“…Generally speaking, these methods can be divided into knowledge-driven, data-driven, and a combination of both. Representative knowledge-driven methods include fuzzy logic [7][8][9][10][11][12][13][14][15], fuzzy comprehensive evaluation, the analytic hierarchy process [16][17][18], and the expert system method, while data-driven models mainly include the information value [19], logistic regression [20][21][22][23][24][25][26][27], artificial neural networks [28][29][30], support vector machines [31][32][33][34], and other machine learning methods. The study in [3] found that the logistic regression model seemed to be the most popular method in LSM, which was also used in this study.…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)

Song

Niu

et al. 2018

IJGI

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The main goal of this study is to produce a landslide susceptibility map in the Wanzhou section of the Three Gorges reservoir area (China) with a weighted gradient boosting decision tree (weighted GBDT) model. According to the current research on landslide susceptibility mapping (LSM), the GBDT method is rarely used in LSM. Furthermore, previous studies have rarely considered the imbalance of landslide samples and simply regarded the LSM problem as a binary classification problem. In this paper, we considered LSM as an imbalanced learning problem and obtained a better predictive model using the weighted GBDT method. The innovations of the article mainly include the following two points: introducing the GBDT model into the evaluation of landslide susceptibility; using the weighted GBDT method to deal with the problem of landslide sample imbalance. The logistic regression (LR) model and gradient boosting decision tree (GBDT) model were also used in the study to compare with the weighted GBDT model. Five kinds of data from different data source were used in the study: geology, topography, hydrology, land cover, and triggered factors (rainfall, earthquake, land use, etc.). Twenty nine environmental parameters and 233 landslides were used as input data. The receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, and the recall value were used to estimate the quality of the weighted GBDT model, the GBDT model, and the LR model. The results showed that the GBDT model and the weighted GBDT model had a higher AUC value (0.977, 0.976) than the LR model (0.845); the weighted GBDT model had a little higher AUC value (0.977) than the GBDT model (0.976); and the weighted GBDT model had a higher recall value (0.823) than the GBDT model (0.426) and the LR model (0.004). The weighted GBDT method could be considered to have the best performance considering the AUC value and the recall value in landslide susceptibility mapping dealing with imbalanced landslide data.

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“…Westen et al (1997) classified the general techniques of analyzing landslide zoning using GIS techniques into heuristic, statistical and deterministic approaches. More recently, some researchers have created landslide susceptibility maps using statistical models, and some of them combine those models with other approaches such as frequency ratio (FR) and logistic regression (LR) methods (e.g., by Lee and Pradhan 2007, Oh et al 2008, and Solaimani et al 2013. FR was combined with analytical heuristic approach (AHP) by Demir et al (2013) and Reis et al (2012), and combination using FR, AHP, LR and artificial neural network (ANN) model was proposed by Park et al (2013).…”

Section: Introductionmentioning

confidence: 99%

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

Rasyid

Bhandary

Yatabe

2016

Geoenviron Disasters

177

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The purposes of this study is to create a landslide susceptibility map (LSM) for Lompobattang Mountain area in Indonesia. The foot of the Lompobattang Mountain area suffered flash flood and landslides in 2006, which led to significant adverse impact on the nearby settlements. There were 158 identified landslides covering a total area of 3.44 km 2 . Landslide inventory data were collected using google earth image interpretations. The landslide inventories were prepared out of the past landslide events, and future landslide occurrence was predicted by correlating landslide causal factors. In this study landslide inventories were divided into landslide data for training and landslide data for validation. The LSM was prepared by Frequency Ratio (FR) and Logistic Regression (LR) statistical methods. Lithology, distance from the road, distance from the river, distance from the fault, land use, curvature, aspect, and slope degree were used as conditioning parameters. Area under the curve (AUC) of the Receiver Operating Characteristic (ROC) was used to check the performance of the models. In the analysis, the FR model results in 85.8 % accuracy in the AUC success rate while the LR model was found to have 86.9 % accuracy. However, the accuracy of both these models in AUC predictive rate is the same at around 85.1 %. The LR model is 6.34 % higher than the FR model in comparison to its accuracy for ratio of landslide validation. The landslide susceptibility map consist of the predicted landslide area, hence it can be used to reduce the potential hazard associated with the landslides in this study area.

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Landslide susceptibility mapping based on frequency ratio and logistic regression models

Cited by 104 publications

References 56 publications

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Predictive modeling of landslide hazards in Wen County, northwestern China based on information value, weights-of-evidence, and certainty factor

Landslide Susceptibility Mapping Based on Weighted Gradient Boosting Decision Tree in Wanzhou Section of the Three Gorges Reservoir Area (China)

Performance of frequency ratio and logistic regression model in creating GIS based landslides susceptibility map at Lompobattang Mountain, Indonesia

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