Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data

Wang, Yiting; Seijmonsbergen, A.C.; Bouten, W.; Chen, Qingtao

doi:10.1007/s11629-014-3134-x

Cited by 20 publications

(8 citation statements)

References 49 publications

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“…Furthermore, some scholars have also analyzed and compared landslide susceptibility through qualitative and quantitative aspects in Shaanxi Province, such as FR-AHP [36], certainty factor (CF), and index of entropy (IOE) [37] model. In addition, compared with other models, SVM model shows better prediction results [38]. However, there is no further study on quantitative analysis.…”

Section: Introductionmentioning

confidence: 95%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

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The main purpose of this research is to apply the logistic regression (LR) model, the support vector machine (SVM) model based on radial basis function, the random forest (RF) model, and the coupled model of the whale optimization algorithm (WOA) and genetic algorithm (GA) with RF, to make landslide susceptibility mapping for the Ankang City of Shaanxi Province, China. To this end, a landslide inventory map consisting of 4278 identified landslides is randomly divided into training and test landslides in a ratio of 7 : 3. The 15 landslide influencing factors are selected as follows: slope aspect, slope degree, elevation, terrain curvature, plane curvature, profile curvature, surface roughness, distance to faults, distance to roads, landform, lithology, distance to rivers, rainfall, stream power index (SPI), and normalized difference vegetation index (NDVI), and the potential multicollinearity problem among these factors is detected by Pearson correlation coefficient (PCC), variance inflation factor (VIF), and tolerance (TOL). We evaluate the performance of the model separately by statistical training and test dataset metrics, including sensitivity, specificity, accuracy, kappa, mean absolute error (MSE), root mean square error (RMSE), and area under the receiver operating characteristic curve. The training success rates of LR, SVM, RF, WOA-RF, and GA-RF models are 0.7546, 0.8317, 0.8561, 0.8804, and 0.8957; the testing success rates are 0.7551, 0.8375, 0.8395, 0.8348, and 0.85007. The results show that the GA significantly improves the predictive power of the RF model. This study provides a scientific reference for disaster prevention and control in this area and its surrounding areas.

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Section: Introductionmentioning

confidence: 95%

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Zhao

Zhu

et al. 2022

Geofluids

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“…In this paper, the relationship between the occurrence of landslides (the dependent variable) and multiple hazard factors is described (Menard, 1995;Atkinson and Massari, 1998). The independent variable in this model can be continuous or discrete, and it does not need to satisfy a normal frequency distribution (Bai et al, 2015;Wang et al, 2015). The expression of the logistic regression core function is…”

Section: Logistic Regression Modelmentioning

confidence: 99%

Landslide susceptibility assessment using the certainty factor and deep neural network

Jiang

Wei

et al. 2023

Front. Earth Sci.

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Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.

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“…También se ha geolocalizado la parte superior y no movilizada del escarpe principal, verificando y corrigiendo las coordenadas de cada punto con la aplicación de Google Earth. La localización de estos puntos en las capas temáticas espaciales ha permitido conocer las características del ámbito anteriores al deslizamiento (Wang et al, 2015). Posteriormente, se ha creado una capa de puntos de distribución espacial con la ayuda del software ArcGIS 10.0.…”

Section: Inventario De Deslizamientosunclassified

“…Además, con el fin de minimizar el impacto del tamaño de los deslizamientos (Dai y Lee, 2002), se ha excluido un buffer de 30 m alrededor de cada punto de inestabilidad para evitar que los puntos estables caigan dentro de las áreas deslizadas. Así, se han considerado aquellos lugares sin ningún deslizamiento de tierra inventariado como espacios libres de inestabilidades (Nefeslioglu et al, 2008;Pourghasemi et al, 2013;Wang et al, 2015).…”

Section: Inventario De Deslizamientosunclassified

Mapas de susceptibilidad de deslizamientos a partir del modelo de regresión logística en la cuenca del río Oria (Gipuzkoa). Estrategias de tratamiento de variables

Bornaetxea

Antigüedad

Ormaetxea

2018

CyG

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Using statistical learning algorithms in regional landslide susceptibility zonation with limited landslide field data

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Cited by 20 publications

References 49 publications

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Mapping Landslide Sensitivity Based on Machine Learning: A Case Study in Ankang City, Shaanxi Province, China

Landslide susceptibility assessment using the certainty factor and deep neural network

Mapas de susceptibilidad de deslizamientos a partir del modelo de regresión logística en la cuenca del río Oria (Gipuzkoa). Estrategias de tratamiento de variables

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