GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

Chen, Wei; Li, Yang

doi:10.1016/j.catena.2020.104777

Cited by 178 publications

(55 citation statements)

References 96 publications

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“…The performance of LSMs depends on the choice of landslide conditioning factors. Numerous studies on LSM have been conducted based on machine learning techniques [1,16,18,23,26,37,38], with various combinations of landslide conditioning factors being used. However, the selection of factors should be (i) based on their degree of affinity with landslide locations, (ii) measurable, (iii) non-redundant, and (iv) based on the knowledge of geomorphological characteristics of the area under study [2].…”

Section: Landslide Conditioning Factormentioning

confidence: 99%

“…Areas with an elevation of less than 5 m, as well as, waterbodies and sandy sea beach areas (waterbody and restricted in Figure 4) were excluded from the LSMs [39]. Topographical and hydrological parameters including aspect, elevation, slope, curvature, and Stream Power Index (SPI) are important factors that limit the density and spatial extent of landslides [2,37,38,40]. Raster maps of aspect, elevation, curvature, slope, and SPI were derived at 30-m spatial resolution from the Advanced Land Observing Satellite (ALOS) Digital Elevation Model (DEM) [28] (Figure 4a-e).…”

Section: Landslide Conditioning Factormentioning

confidence: 99%

“…Only a limited number of studies, conducted on a specific basin, found that landslides occur at certain elevations [41]. Elevation can determine the spatial variability of landslides because it is affected by geological tectonics [37]. It can also influence the occurrence of landslides by impacting other causative factors such as slope, curvature, and SPI [42].…”

Section: Landslide Conditioning Factormentioning

confidence: 99%

“…It can also influence the occurrence of landslides by impacting other causative factors such as slope, curvature, and SPI [42]. Aspect, indicating the direction of slope [43], indirectly influences the distribution of landslide locations by affecting the general physiographic trend of the area and/or the main precipitation direction [37,40]. Slope angle is considered as one of the most influential factors for the occurrence of landslides, as it affects the concentration of moisture and the level of pore pressure, as well as, controls regional hydraulic continuity [2,40].…”

Section: Landslide Conditioning Factormentioning

confidence: 99%

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Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

et al. 2020

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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

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Section: Landslide Conditioning Factormentioning

confidence: 99%

Section: Landslide Conditioning Factormentioning

confidence: 99%

Section: Landslide Conditioning Factormentioning

confidence: 99%

Section: Landslide Conditioning Factormentioning

confidence: 99%

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Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

et al. 2020

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“…We used the receiver operating characteristics (ROC) approach to corroborate the six flood susceptibility maps obtained through the FR, AHP, EBF, FR geons, AHP geons, and EBF geons using the validation data. The ROC method displays the assessment results in terms of the true positive rate (TPR) and the false positive rate (FPR) in the resulting flood susceptibility maps (Linden 2006;Ghorbanzadeh et al 2018c;Chen and Li 2020;Lei et al 2020b). Pixels that are correctly referred to as having a high susceptibility in the flood validation data are the TPRs, whereas the incorrectly labelled pixels are the FPRs.…”

Section: Receiver Operating Characteristics (Roc)mentioning

confidence: 99%

A novel per pixel and object-based ensemble approach for flood susceptibility mapping

Nachappa

Meena

2020

Geomatics, Natural Hazards and Risk

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Conducting flood susceptibility assessments is critical for the identification of flood hazard zones and the mitigation of the detrimental impacts of floods in the future through improved flood management measures. The significance of this study is that we create ensemble methods using the per-pixel approaches of frequency ratio (FR), analytical hierarchical process (AHP), and evidence belief function (EBF) used for weightings with the object-based 'geons' approach used for aggregation to create a flood susceptibility map for the East Rapti Basin in Nepal. We selected eight flood conditioning factors considered to be relevant in the study area. The flood inventory data for the East Rapti basin was derived from past flood inventory datasets held in the regional database system by the International Centre for Integrated Mountain Development (ICIMOD). The flood inventory was classified into training and validation datasets based on the widely used split ratio of 70/30. The Receiver Operating Characteristic (ROC) was used to determine the accuracy of the flood susceptibility maps. The AUC results indicated that the combined per-pixel and object-based geon approaches yielded better results than the per-pixel approaches alone. Our results showed that the object-based geon approach creates meaningful regional units that are beneficial for future planning.

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Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

Zhang

Liu

et al. 2023

Geological Journal

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Landslide susceptibility analysis can provide theoretical support for landslide risk management. However, some susceptibility analyses are not sufficiently interpretable. Moreover, the accuracy of many research methods needs to be improved. Therefore, this study can supplement these deficiencies. This study aims to research the evaluation effects of random forest (RF) and extreme gradient boosting (XGBoost) classifier models on landslide susceptibility, and to compare their applicability in Fengjie County, Chongqing, a typical landslide‐prone area in southwest of China. Firstly, 1624 landslides information from 1980 to 2020 were obtained through field investigation, and a geospatial database of 16 conditional factors had been constructed. Secondly, non‐landslide points were selected to form a complete data set and RF and XGBoost models were established. Finally, the area under the ROC curve (AUC) value, accuracy, and F‐score were used to compare the two models. The results show that even though both classifiers have a highly accurate evaluation of landslide susceptibility, the RF model performs better. In comparison, the RF model has a higher AUC value of 0.866, and its accuracy and F‐score are approximately 2% higher than XGBoost. The land use, elevation, and lithology of Fengjie County contribute to the occurrence of landslides. This is due to human engineering activities (such as land reclamation, and housing construction) resulting in low slope stability and landslides in widely distributed sandstone, siltstone, and mudstone layers owing to their low permeability and planes of weakness.

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GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

Cited by 178 publications

References 96 publications

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping

A novel per pixel and object-based ensemble approach for flood susceptibility mapping

Landslide Susceptibility mapping using random forest and extreme gradient boosting: A case study of Fengjie, Chongqing

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