Abstract
Logistic regression methods have been widely used for landslide research. However, previous studies have seldom paid attention to the frequent occurrence of spatial autocorrelated residuals in regression models, which indicate a model misspecification problem and unreliable results. This study accounts for spatial autocorrelation by implementing eigenvector spatial filtering (ESF) into logistic regression for landslide susceptibility assessment. Based on a landslide inventory map and 11 landslide predisposing factors, we developed the eigenvector spatial filtering-based logistic regression (ESFLR) model, as well as a conventional logistic regression (LR) model and an autologistic regression (ALR) model for comparison. The three models were evaluated and compared in terms of their prediction capability and model fit. The ESFLR model performed better than the other two models. The overall predictive accuracy of the ESFLR model was 90.53%, followed by the ALR model (76.21%) and the LR model (74.76%), and the areas under the ROC curves for the ESFLR, ALR and LR models were 0.957, 0.828 and 0.818, respectively. The ESFLR model adequately addressed the spatial autocorrelation of residuals by reducing the Moran’s I value of the residuals to 0.0270. In conclusion, the ESFLR model is an effective and flexible method for landslide analysis.
Highlights
Landslides are the most common natural geological disasters and cause damage to infrastructure and natural ecology, resulting in serious casualties and economic losses [1]
The overall accuracy measures the overall classification ability of the models, and the value is 90.53 for the eigenvector spatial filtering-based logistic regression (ESFLR) model, 76.21 for the autologistic regression (ALR) model, and 74.76 for the logistic regression (LR) model. These results clearly suggest that the ESFLR model is superior to the other two models in terms of classification accuracy for both landslides and non-landslides
The ESFLR model was constructed for landslide susceptibility assessment by introducing eigenvector spatial filtering into conventional logistic regression to explain the spatial patterns of the dependent variables inherent in the model residuals
Summary
Landslides are the most common natural geological disasters and cause damage to infrastructure and natural ecology, resulting in serious casualties and economic losses [1]. Previous landslide studies using logistic regression for landslide prediction rarely took spatial autocorrelation into account [20], which implies that these models failed to explain all of the spatial patterns inherent in the landslide data, and may have led to misspecification errors in the model. This study attempted to eliminate the negative influence of spatial autocorrelation on landslide susceptibility assessments by introducing eigenvector spatial filtering (ESF) into logistic regression. The ESF method proposed by Griffith, utilizes eigenvectors generated from a given spatial connectivity matrix to account for redundant locational information resulting from spatial autocorrelation [24]. Several significant eigenvectors selected with a stepwise regression procedure are added to the linear regression model as independent variables to filter the spatial autocorrelation out from the regression residuals. The models were employed to map landslide susceptibility throughout the study area
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