Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea

Lee, Moung-Jin; Park, Inhye; Lee, Saro

doi:10.1007/s12665-015-4048-9

Cited by 57 publications

(33 citation statements)

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“…From the validation of the landslide susceptibility maps, the RBF kernel produced AUC values, indicating the accuracy of the landslide susceptibility maps, and these were 81.36% for the PyeongChang area, and 77.49% for the Inje area (Figure 7). There were some differences in accuracy between the study areas, because the previous studies [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][29][30][31][32][33] showed that the spatial distribution is subject to change, according to the area and event. However, the accuracy was usually high enough, displaying figures of above 80%.…”

Section: Resultsmentioning

confidence: 99%

“…The selected factors are assumed to have a dominant influence on the occurrence of landslides. Previous studies have analyzed these factors using the same parameters and frequency ratio model in South Korea, including a similar area [13,[29][30][31][32][33]. The probability-likelihood ratio method was applied to Boun, Korea [29], and the probability logistic regression method was used for the statistical analysis of land slide susceptibility at Yongin, Korea [30].…”

Section: Datamentioning

confidence: 99%

“…Landslide detection was applied with the frequency ratio, weight of evidence, logistic regression, and artificial neural network models in Jinbu, Korea [31]. The decision tree was used for the landslide susceptibility mapping in Pyeongchang, Korea [13], and the integration of frequency ratio and neuro-fuzzy models was used to forecast and validate the landslide susceptibility in the Seorak mountain area [32]. Landslides in the Inje area were mapped using the frequency ratio with the condition of rainfall probability [33].…”

Section: Datamentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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Abstract:In this study, the support vector machine (SVM) was applied and validated by using the geographic information system (GIS) in order to map landslide susceptibility. In order to test the usefulness and effectiveness of the SVM, two study areas were carefully selected: the PyeongChang and Inje areas of Gangwon Province, Korea. This is because, not only did many landslides (2098 in PyeongChang and 2580 in Inje) occur in 2006 as a result of heavy rainfall, but the 2018 Winter Olympics will be held in these areas. A variety of spatial data, including landslides, geology, topography, forest, soil, and land cover, were identified and collected in the study areas. Following this, the spatial data were compiled in a GIS-based database through the use of aerial photographs. Using this database, 18 factors relating to topography, geology, soil, forest and land use, were extracted and applied to the SVM. Next, the detected landslide data were randomly divided into two sets; one for training and the other for validation of the model. Furthermore, a SVM, specifically a type of data-mining classification model, was applied by using radial basis function kernels. Finally, the estimated landslide susceptibility maps were validated. In order to validate the maps, sensitivity analyses were carried out through area-under-the-curve analysis. The achieved accuracies from the SVM were approximately 81.36% and 77.49% in the PyeongChang and Inje areas, respectively. Moreover, a sensitivity assessment of the factors was performed. It was found that all of the factors, except for soil topography, soil drainage, soil material, soil texture, timber diameter, timber age, and timber density for the PyeongChang area, and timber diameter, timber age, and timber density for the Inje area, had relatively positive effects on the landslide susceptibility maps. These results indicate that SVMs can be useful and effective for landslide susceptibility analysis.

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Section: Resultsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

2017

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“…The models include frequency ratio [14,15], weight of evidence [16], logistic regression [17], and fuzzy logic [18]. Recently, data mining techniques have been developed and are extremely popular [19,20] when dealing with a variety of nonlinear issues.…”

Section: Introductionmentioning

confidence: 99%

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Lee

2017

Applied Sciences

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Abstract:The main purpose of this paper is to present some potential applications of sophisticated data mining techniques, such as artificial neural network (ANN) and boosted tree (BT), for landslide susceptibility modeling in the Yongin area, Korea. Initially, landslide inventory was detected from visual interpretation using digital aerial photographic maps with a high resolution of 50 cm taken before and after the occurrence of landslides. The debris flows were randomly divided into two groups: training and validation sets with a 50:50 proportion. Additionally, 18 environmental factors related to landslide occurrence were derived from the topography, soil, and forest maps. Subsequently, the data mining techniques were applied to identify the influence of environmental factors on landslide occurrence of the training set and assess landslide susceptibility. Finally, the landslide susceptibility indexes from ANN and BT were compared with a validation set using a receiver operating characteristics curve. The slope gradient, topographic wetness index, and timber age appear to be important factors in landslide occurrence from both models. The validation result of ANN and BT showed 82.25% and 90.79%, which had reasonably good performance. The study shows the benefit of selecting optimal data mining techniques in landslide susceptibility modeling. This approach could be used as a guideline for choosing environmental factors on landslide occurrence and add influencing factors into landslide monitoring systems. Furthermore, this method can rank landslide susceptibility in urban areas, thus providing helpful information when selecting a landslide monitoring site and planning land-use.

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“…Moreover, they have applied the logistic regression model to landslide hazard mapping (Lee and Pradhan 2006;Choi et al 2012). Recently, landslide hazard evaluation carried out by using fuzzy logic, and artificial neural network models Yilmaz 2010;Lee et al 2014). During the last decade, researchers indicated that landslide susceptibility and deformation measurement have extensively performed particularly for the landslides assessment (Luzi et al 2000;Schulz 2004;Su and Bork 2006;Streutker and Glenn 2006;Schulz 2007).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs

Pirasteh

2017

Geoenviron Disasters

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This paper expands the previous efforts by other researchers to present a quantitative and deterministic approach for terrain analysis. This study evaluates both spatial and temporal factors contributing landslides utilizing Light Detection and Ranging (LiDAR) point clouds in conjunction with the frequency ratio model (PFR) than has previously been used in the Alborz Mountains. The study area is Marzan Abad of the Alborz Mountain in Iran. The significance of this study is the performance of a high-resolution digital elevation model (DEM) derived from LiDAR point clouds in order to provide detailed information in improving landslide susceptibility evaluation. This study discusses how we improve the quality of landslide susceptibility evaluation. We apply the PFR model to consider the effect of landslide-related factors associated with Google Earth's high-resolution images and field observations. The LiDAR point cloud data and GIS-based analysis have allowed performing high quality ways of landslide hazard assessments using inventory dataset as compared to previous studies. We contributed an improved landslide inventory map of the Mazandaran Province. We used image elements interpretation from the available ASTER DEM (30 m), LiDAR-DEM (5 m), and the Google Earth high spatial resolution images in conjunction with the field observations. This study evaluates factors such as geology, geomorphology, landuse, soil, slope, and distance from roads and drainage to represent, manipulate, and analyze factors. Also, we evaluated the performance success of the rate curve of landslides susceptibility. The results have indicated an improved landslide susceptibility map from LiDAR-derived DEMs implementing the PFR model with 92.59% of accuracy performance as compared to the existing data and previous studies in the same region. Furthermore, this study reveals that all considering factors have relatively positive effects on the landslides susceptibility mapping in the study, however, the most effective factor on the landslide occurrence is the lithology with 13.7%.

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Forecasting and validation of landslide susceptibility using an integration of frequency ratio and neuro-fuzzy models: a case study of Seorak mountain area in Korea

Cited by 57 publications

References 50 publications

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

A Support Vector Machine for Landslide Susceptibility Mapping in Gangwon Province, Korea

Shallow Landslide Susceptibility Modeling Using the Data Mining Models Artificial Neural Network and Boosted Tree

Probabilistic frequency ratio (PFR) model for quality improvement of landslide susceptibility mapping from LiDAR-derived DEMs

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