Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression

Chalkias, Christos; Polykretis, Christos; Karymbalis, Efthimios; Soldati, Mauro; Ghinoi, Alessandro; Ferentinou, Maria

doi:10.1007/s10064-020-01733-x

Cited by 20 publications

(8 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with the assessment method of LSA, the study of the essential attribute, namely the spatial non-stationarity, of the landslide as a geospatial phenomenon is insufficient. At present, a small number of scholars have carried out studies on the consideration of spatial non-stationarity and the application of the GWR idea in LSA, and have proved that the GWR model is superior to some traditional models, such as global linear regression (GLR) [21,33], ANN and OLS [73], SVM [74], and SR [30]. However, there is a research gap in the study of the non-stationarity scale of the spatial relationship between landslides and LCFs.…”

Section: Discussionmentioning

confidence: 99%

“…The statistical analysis includes bivariate and multivariate techniques [18,19]. Multivariate statistical analysis is mainly expressed in regression models, which consist of global regression models, such as logistic regression [20], and local regression models, such as Geographically Weighted Regression (GWR) [21]. The global regression models consider the influence of LCFs on landslides as stable for a region.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Huang

et al. 2022

Water

View full text Add to dashboard Cite

Landslide Susceptibility Assessment (LSA) is a fundamental component of landslide risk management and a substantial area of geospatial research. Previous researchers have considered the spatial non-stationarity relationship between landslide occurrences and Landslide Conditioning Factors (LCFs) as fixed effects. The fixed effects consider the spatial non-stationarity scale between different LCFs as an average value, which is represented by a single bandwidth in the Geographically Weighted Regression (GWR) model. The present study analyzes the non-stationarity scale effect of the spatial relationship between LCFs and landslides and explains the influence of factor correlation on the LSA. A Principal-Component-Analysis-based Multiscale GWR (PCAMGWR) model is proposed for landslide susceptibility mapping, in which hexagonal neighborhoods express spatial proximity and extract LCFs as the model input. The area under the receiver operating characteristic curve and other statistical indicators are used to compare the PCAMGWR model with other GWR-based models and global regression models, and the PCAMGWR model has the best prediction effect. Different spatial non-stationarity scales are obtained and improve the prediction accuracy of landslide susceptibility compared to a single spatial non-stationarity scale.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Huang

et al. 2022

Water

View full text Add to dashboard Cite

show abstract

“…In fact, they represent the relation between landslide occurrence and causal factors for the whole study area without considering the spatial non-stationarity. Further studies are also to be focused on the exploration of this non-stationarity through the implementation of "local" models like geographically weighted regression [138].…”

Section: Discussionmentioning

confidence: 99%

Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Conforti

Ietto

2021

Geosciences

View full text Add to dashboard Cite

Shallow landslides are destructive hazards and play an important role in landscape processes. The purpose of this paper is to evaluate the shallow landslide susceptibility and to investigate which predisposing factors control the spatial distribution of the collected instability phenomena. The GIS-based logistic regression model and jackknife test were respectively employed to achieve the scopes. The studied area falls in the Mesima basin, located in the southern Calabria (Italy). The research was based mainly on geomorphological study using both interpretation of Google Earth images and field surveys. Thus, 1511 shallow landslides were mapped and 18 predisposing factors (lithology, distance to faults, fault density, land use, soil texture, soil bulk density, soil erodibility, distance to streams, drainage density, elevation, slope gradient, slope aspect, local relief, plan curvature, profile curvature, TPI, TWI, and SPI) were recognized as influencing the shallow landslide susceptibility. The 70% of the collected shallow landslides were randomly divided into a training data set to build susceptibility model and the remaining 30% were used to validate the newly built model. The logistic regression model calculated the landslide probability of each pixel in the study area and produced the susceptibility map. Four classification methods were tested and compared between them, so the most reliable classification system was employed to the shallow landslide susceptibility map construction. In the susceptibility map, five classes were recognized as following: very low, low, moderate, high, and very high susceptibility. About 26.1% of the study area falls in high and very high susceptible classes and most of the landslides mapped (82.4%) occur in these classes. The accuracy of the predictive model was evaluated by using the ROC (receiver operating characteristics) curve approach, which showed an area under the curve (AUC) of 0.93, proving the excellent forecasting ability of the susceptibility model. The predisposing factors importance evaluation, using the jackknife test, revealed that slope gradient, TWI, soil texture and lithology were the most important factors; whereas, SPI, fault density and profile curvature have a least importance. According to these results, we conclude that the shallow landslide susceptibility map can be use as valuable tool both for land-use planning and for management and mitigation of the shallow landslide risk in the study area.

show abstract

“…Landslide susceptibility assessment aims to estimate the spatial probability of potential unstable slopes based on the information of past and present landslide events. The quality of landslide susceptibility map highly depends on the input data, especially the explanatory variables and landslides inventory [6][7][8][9]. Many approaches have been developed to quantitatively assess landslide susceptibility, which could be loosely grouped into three main groups, physically-driven models, knowledge-driven models, data-driven models, including statistically-based classification methods and recently well-developed machine learning (ML) methods [10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

Liu

Yin

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

The power network has a long transmission span and passes through wide areas with complex topography setting and various human engineering activities. They lead to frequent landslide hazards, which cause serious threats to the safe operation of the power transmission system. Thus, it is of great significance to carry out landslide susceptibility assessment for disaster prevention and mitigation of power network. We, therefore, undertake an extensive analysis and comparison study between different data-driven methods using a case study from China. Several susceptibility mapping results were generated by applying a multivariate statistical method (logistic regression (LR)) and a machine learning technique (random forest (RF)) separately with two different mapping-units and predictor sets of differing configurations. The models’ accuracies, advantages and limitations are summarized and discussed using a range of evaluation criteria, including the confusion matrix, statistical indexes, and the estimation of the area under the receiver operating characteristic curve (AUROC). The outcome showed that machine learning method is well suitable for the landslide susceptibility assessment along transmission network over grid cell units, and the accuracy of susceptibility models is evolving rapidly from statistical-based models toward machine learning techniques. However, the multivariate statistical logistic regression methods perform better when computed over heterogeneous slope terrain units, probably because the number of units is significantly reduced. Besides, the high model predictive performances cannot guarantee a high plausibility and applicability of subsequent landslide susceptibility maps. The selection of mapping unit can produce greater differences on the generated susceptibility maps than that resulting from the selection of modeling methods. The study also provided a practical example for landslide susceptibility assessment along the power transmission network and its potential application in hazard early warning, prevention, and mitigation.

show abstract

Exploring spatial non-stationarity in the relationships between landslide susceptibility and conditioning factors: a local modeling approach using geographically weighted regression

Cited by 20 publications

References 68 publications

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Spatial Non-Stationarity-Based Landslide Susceptibility Assessment Using PCAMGWR Model

Modeling Shallow Landslide Susceptibility and Assessment of the Relative Importance of Predisposing Factors, through a GIS-Based Statistical Analysis

Susceptibility Assessment for Landslide Initiated along Power Transmission Lines

Contact Info

Product

Resources

About