Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China

Bai, Shibiao; Wang, Jian; Zhang, Zhigang; Chen, Cheng

doi:10.1016/j.catena.2012.06.012

Cited by 94 publications

(42 citation statements)

References 21 publications

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“…Such PGA maps are available for most of the seismically affected regions through the Global Seismic Hazard Assessment Project [278]. In the literature, there are some models for earthquake-induced landslides (see a review in [279]), where PGA maps are used as input of the model [280,281]. The validation of the results showed that the ensemble model could provide good prediction rates.…”

Section: Triggering Factorsmentioning

confidence: 99%

“…Coupling ALS data and other RS tools may help the recognition of shallow landslides. ALS DEMs together with high-resolution orthoimagery have proved to be suitable for mapping rainfall-and earthquake-induced landslides [280]. The integration of HR-DEMs from ALS data, aerial photos, TLS data, field surveys, laser rangefinder binocular and GNSS observations found application for inventory of rainfall-induced landslides [45,127].…”

Section: Shallow Landslidesmentioning

confidence: 99%

“…Although their prevalent application is for landslide monitoring, also in landslide prediction SAR data have been exploited. For instance, Bai et al [280] carried out the calibration of a 1D infinite slope model by using the back analysis of continuous GBSAR measurements. Indeed, monitoring information of very-slow landslides can feed numerical models for prediction and for highlighting some active failure processes.…”

Section: Model Validationmentioning

confidence: 99%

See 2 more Smart Citations

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed.

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Section: Triggering Factorsmentioning

confidence: 99%

Section: Shallow Landslidesmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 99%

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Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

et al. 2014

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“…For the statistical model, logistic regression (Bai et al 2011(Bai et al , 2012Yalcin et al 2011;Lepore et al 2012;Devkota et al 2013) have been applied. For the data mining model, artificial neural network (Li et al 2012;Xu et al 2012b;Ramakrishnan et al 2013), neuro-fuzzy logic Li et al 2012;Tien Bui et al 2013), support vector machine (Yilmaz 2010; Xu et al 2012a;Pradhan 2013) and decision Tree (Wan et al 2010;Pradhan 2013) have been applied.…”

mentioning

confidence: 99%

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

2015

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Landslides susceptibility maps were constructed in Seorak mountain area, Korea, using an integration of frequency ratio and adaptive neuro-fuzzy inference system (ANFIS) in geographical information system (GIS) environment. Landslide occurrence areas were detected in the study by interpreting aerial photographs and field survey data. Landslide locations were randomly selected in a 50/50 ratio for training and validation of the models, respectively. Topography, geology, soil, and forest databases were also constructed. Maps relevant to landslide occurrence were assembled in a spatial database. Using the constructed spatial database, 17 landslide-related factors were extracted. The adaptive ANFIS model with different types of membership functions (MFs) was then applied for landslide susceptibility mapping in the study area. Two landslide susceptibility maps were prepared using the different MFs. The frequency ratio model was also applied to the landslide susceptibility mapping for comparing with the probabilistic ANFIS model. Finally, the resulting landslide susceptibility maps were validated using the landslide locations which were not used for training the ANFIS. The validation results showed 75.57 % accuracy using the generalized bell-shaped MF model, 74.94 % accuracy using the Sigmoidal 2 MF model and 73.07 % accuracy using frequency ratio model. These accuracy results show that an ANFIS can be an effective tool in landslide susceptibility mapping.

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“…Previous studies have applied probabilistic models including an AHP: Analytic Hierarchy Process, Arti cial Neural Network, Dempster-Shapfer theory of evidence, fuzzy logic and Monte Carlo methods [1][2][3][4][5][6][7][8][9][10][11] among statistical models, the logistic regression model has also been applied to landslide susceptibility mapping [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. More sophisticated assessments have involved weight of evidence approaches and frequency ratio [25][26][27][28][29][30][31][32][33][34][35][36][37][38] Research on rainfall probability calculation has primarily been limited to improving the rainfall probability predictions accuracy and to studies targeting water resources [39][40][41][42][43].…”

Section: Introductionmentioning

confidence: 99%

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

Lee

Jeon

et al. 2016

Open Geosciences

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Abstract:The 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 arti cial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identi ed based on interpretations of optical remote sensing data (Aerial photographs) followed by eld 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 arti cial 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 arti cial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the arti cial 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 arti cial 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 ndings, we were able to derive a more e ective method for analyzing landslide susceptibility.

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Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China

Cited by 94 publications

References 21 publications

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

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

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

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