Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

Pawłuszek-Filipiak, Kamila; Oreńczak, Natalia; Pasternak, Marta

doi:10.3390/app10186335

Cited by 11 publications

(8 citation statements)

References 54 publications

(119 reference statements)

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“…The landslide susceptibility indices (LSIs) were generated to produce an LSM. LSIs were divided into the five classes of very low, low, moderate, high, and very high susceptibility using the Jenks natural break classification method [24,53,67]. The Jenks natural break classification method is used to determine the arrangement of values into different classes by minimizing and maximizing each class's deviation from the class mean and other groups' means, respectively [12,42,68].…”

Section: Methodsmentioning

confidence: 99%

“…In the study, the slope failure susceptibility due to underlying causative factors [8] was considered. Land cover, lithology, elevation, proximity to roads, drainage, fault lines, slope aspect, and slope angle were considered as causal factors based on literature [53] and the availability of data for the study area. Each causal factor was mapped and divided into the several equal interval classes described in the legend in Figure 4.…”

Section: Causative Factor Selectionmentioning

confidence: 99%

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Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Pasang

Kubíček

2020

Geosciences

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In areas prone to frequent landslides, the use of landslide susceptibility maps can greatly aid in the decision-making process of the socio-economic development plans of the area. Landslide susceptibility maps are generally developed using statistical methods and geographic information systems. In the present study, landslide susceptibility along road corridors was considered, since the anthropogenic impacts along a road in a mountainous country remain uniform and are mainly due to road construction. Therefore, we generated landslide susceptibility maps along 80.9 km of the Asian Highway (AH48) in Bhutan using the information value, weight of evidence, and logistic regression methods. These methods have been used independently by some researchers to produce landslide susceptibility maps, but no comparative analysis of these methods with a focus on road corridors is available. The factors contributing to landslides considered in the study are land cover, lithology, elevation, proximity to roads, drainage, and fault lines, aspect, and slope angle. The validation of the method performance was carried out by using the area under the curve of the receiver operating characteristic on training and control samples. The area under the curve values of the control samples were 0.883, 0.882, and 0.88 for the information value, weight of evidence, and logistic regression models, respectively, which indicates that all models were capable of producing reliable landslide susceptibility maps. In addition, when overlaid on the generated landslide susceptibility maps, 89.3%, 85.6%, and 72.2% of the control landslide samples were found to be in higher-susceptibility areas for the information value, weight of evidence, and logistic regression methods, respectively. From these findings, we conclude that the information value method has a better predictive performance than the other methods used in the present study. The landslide susceptibility maps produced in the study could be useful to road engineers in planning landslide prevention and mitigation works along the highway.

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Section: Methodsmentioning

confidence: 99%

Section: Causative Factor Selectionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Pasang

Kubíček

2020

Geosciences

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“…SCAI is the ratio of the percentage of the pixels of a landslide susceptibility zone to the percentage of existing landslide pixels in that susceptibility zone. The model is considered excellent if the value of the SCAI decreases from a very low to a very high LS class (Arabameri et al, 2020;Pawluszek-Filipiak et al, 2020). The SCAI is calculated using the following equation:…”

Section: Validation Techniquesmentioning

confidence: 99%

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

et al. 2022

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This study assessed landslide susceptibility in Shahpur valley, situated in the eastern Hindu Kush. Here, landslides are recurrent phenomena that disrupt the natural environment, and almost every year, they cause huge property damages and human losses. These damages are expected to escalate in the study area due to the high rate of deforestation in the region, population growth, agricultural expansion, and infrastructural development on the slopes. Landslide susceptibility was assessed by applying “weight of evidence” (WoE) and “information value” (IV) models. For this, the past landslide areas were identified and mapped on the SPOT5 satellite image and were verified from frequent field visits to remove the ambiguities from the initial inventory. Seven landslide contributing factors including surface geology, fault lines, slope aspect and gradient, land use, and proximity to roads and streams were identified based on indigenous knowledge and studied scientific literature. The relationship of landslide occurrence with contributing factors was calculated using WoE and IV models. The susceptibility maps were generated based on both the WoE and IV models. The results showed that the very high susceptible zone covered an area of 14.49% and 12.84% according to the WoE and IV models, respectively. Finally, the resultant maps were validated using the success and prediction rate curves, seed cell area index (SCAI), and R-index approaches. The success rate curve validated the results at 80.34% for WoE and 80.13% for the IV model. The calculated prediction rate for both WoE and IV was 83.34 and 85.13%, respectively. The SCAI results showed similar performance of both models in landslide susceptibility mapping. The result shows that the R-index value for the very high LS zone was 29.64% in the WoE model, and it was 31.21% for the IV model. Based on the elements at risk, a landslide vulnerability map was prepared that showed high vulnerability to landslide hazards in the lower parts of the valley. Similarly, the hazard and vulnerability maps were combined, and the risk map of the study area was generated. According to the landslide risk map, 5.5% of the study area was under high risk, while 2% of the area was in a very high-risk zone. It was found from the analysis that for assessing landslide susceptibility, both the models are suitable and applicable in the Hindu Kush region.

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“…The results of spatial relationship between landslide locations and related conditioning factors using the frequency ratio (FR) model are shown in Appendix A Table A1. The frequency ratio method can evaluate the sub-classes of specific factors and provide useful instructions for decision-makers to understand the conditioning factors related to landslides and make better policies [11,16,30]. The higher FR value shows that landslide hazards are more prone to occur in corresponding zone [69].…”

Section: Conditioning Factors Analyses Using Frequency Ratiomentioning

confidence: 99%

“…Quantitative methods mainly depend on the relationship between influencing factors and landslide occurrences and can be grouped into two categories, i.e., physically-based methods and data-driven approaches. Physically-based methods assess landslide susceptibility based on simplified physically modeling strategy [6], while data-driven approaches develop a functional relationship between conditioning factors and the past and historical landslide events [7], including weights of evidence [8][9][10], frequency ratio [11], random forest [12,13], artificial neural network (ANN) [14,15], convolutional neural networks [16,17], and support vector machine (SVM) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

et al. 2021

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This study presents a new ensemble framework to predict landslide susceptibility by integrating decision trees (DTs) with the rotation forest (RF) ensemble technique. The proposed framework mainly includes four steps. First, training and validation sets are randomly selected according to historical landslide locations. Then, landslide conditioning factors are selected and screened by the gain ratio method. Next, several training subsets are produced from the training set and a series of trained DTs are obtained by using a DT as a base classifier couple with different training subsets. Finally, the resultant landslide susceptibility map is produced by combining all the DT classification results using the RF ensemble technique. Experimental results demonstrate that the performance of all the DTs can be effectively improved by integrating them with the RF ensemble technique. Specifically, the proposed ensemble methods achieved the predictive values of 0.012–0.121 higher than the DTs in terms of area under the curve (AUC). Furthermore, the proposed ensemble methods are better than the most popular ensemble methods with the predictive values of 0.005–0.083 in terms of AUC. Therefore, the proposed ensemble framework is effective to further improve the spatial prediction of landslides.

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Investigating the Effect of Cross-Modeling in Landslide Susceptibility Mapping

Cited by 11 publications

References 54 publications

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Landslide Susceptibility Mapping Using Statistical Methods along the Asian Highway, Bhutan

Assessment of landslide susceptibility, exposure, vulnerability, and risk in shahpur valley, eastern hindu kush

Landslide Susceptibility Mapping Using Rotation Forest Ensemble Technique with Different Decision Trees in the Three Gorges Reservoir Area, China

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