GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China

Yu, Zhonghua; Wang, Rui; Jiang, Yuanjun; Liu, Huajun; Wei, Zhenlei

doi:10.1016/j.enggeo.2019.105147

Cited by 104 publications

(48 citation statements)

References 84 publications

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“…The SVM algorithm is built upon a hyperplane or a set of hyperplanes in a high-or infinite-dimensional space, and can be used for separating landslide and non-landslide cases. The two models have been successful used in many LSM tasks [68][69][70][71][72][73]. regression (LR) and support vector machine (SVM).…”

Section: Training Validation and Independent Testingmentioning

confidence: 99%

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

et al. 2020

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A serious earthquake could trigger thousands of landslides and produce some slopes more sensitive to slide in future. Landslides could threaten human’s lives and properties, and thus mapping the post-earthquake landslide susceptibility is very valuable for a rapid response to landslide disasters in terms of relief resource allocation and posterior earthquake reconstruction. Previous researchers have proposed many methods to map landslide susceptibility but seldom considered the spatial structure information of the factors that influence a slide. In this study, we first developed a U-net like model suitable for mapping post-earthquake landslide susceptibility. The post-earthquake high spatial airborne images were used for producing a landslide inventory. Pre-earthquake Landsat TM (Thematic Mapper) images and the influencing factors such as digital elevation model (DEM), slope, aspect, multi-scale topographic position index (mTPI), lithology, fault, road network, streams network, and macroseismic intensity (MI) were prepared as the input layers of the model. Application of the model to the heavy-hit area of the destructive 2008 Wenchuan earthquake resulted in a high validation accuracy (precision 0.77, recall 0.90, F1 score 0.83, and AUC 0.90). The performance of this U-net like model was also compared with those of traditional logistic regression (LR) and support vector machine (SVM) models on both the model area and independent testing area with the former being stronger than the two traditional models. The U-net like model introduced in this paper provides us the inspiration that balancing the environmental influence of a pixel itself and its surrounding pixels to perform a better landslide susceptibility mapping (LSM) task is useful and feasible when using remote sensing and GIS technology.

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Section: Training Validation and Independent Testingmentioning

confidence: 99%

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

et al. 2020

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“…According to Balzano et al [2], Roy et al [28], Zhao et al [31], the first and foremost move for the assessing of an area's landslide susceptibility is to examine, locate and map the landslides. This may be achieved by field surveys or via highresolution aerial imagery.…”

Section: Landslide Inventory Mappingmentioning

confidence: 99%

“…A large number of researchers produced LSZ maps using frequency ratio (FR) and binary logistic regression (BLR) models [5,[28][29][30]. The relationship between LS site and landslideconditioning factors can be analyzed through the BLR model [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

2020

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Kurseong municipality and its surrounding hill slope are famous high altitudinal residential areas in the Darjeeling Himalayan region of India. In Darjeeling Himalayas, huge landslides occur in the rainy season every year. This paper was aimed to delineate landslide susceptibility zone (LSZ) in Kurseong and its surrounding hill slope in Darjeeling Himalayas. Nine landslide inducing parameters (i.e., slope, altitude, rainfall, geological structure, distance from river channels, distance from lineament, soil type or characteristics, land-use/land-cover and aspect) were used for mapping LSZ applying analytic hierarchy process (AHP), frequency ratio (FR), binary logistic regression (BLR) models and their ensemble (AHP-FR; AHP-BLR; and FR-BLR) with the aid of ArcGIS, SPSS and ERDAS software. The landslide susceptible maps produced in GIS environment by the AHP, FR, BLR and their ensemble models were classified into five susceptibility classes such as very low, low, moderate, high and very high. The area under curve (AUC) of receiver operating characteristics (ROC) and kappa statistics were used to analyze the accuracy of the LSZ maps. The AUC values of prepared maps were 78.86% (AHP), 80.22% (FR), 80.67% (BLR), 83.44% (AHP-FR), 84.39% (AHP-BLR) and 84.73% (FR-BLR). Overall kappa statistics were 0.789 (AHP), 0.812 (FR), 0.799 (BLR), 0.837 (AHP-FR), 0856 (AHP-BLR) and 0.868 (FR-BLR), indicating the good accuracy of the models. The mapping of LSZ will be useful for land-use planning and future landslide hazard mitigation strategies.

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“…One of the essential procedures to reduce damage and fatalities caused by landslides is landslide susceptibility mapping (LSM) [5][6][7][8][9]. Trigila et al [10] reported that LSM is the first step in estimating landslide hazard and risk.…”

Section: Introductionmentioning

confidence: 99%

Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

Akıncı

Kılıçoğlu

Doğan

2020

IJGI

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Natural disasters such as landslides often occur in the Eastern Black Sea region of Turkey owing to its geological, topographical, and climatic characteristics. Landslide events occur nearly every year in the Arhavi, Hopa, and Kemalpaşa districts located on the Black Sea coast in the Artvin province. In this study, the landslide susceptibility map of the Arhavi, Hopa, and Kemalpaşa districts was produced using the random forest (RF) model, which is widely used in the literature and yields more accurate results compared with other machine learning techniques. A total of 10 landslide-conditioning factors were considered for the susceptibility analysis, i.e., lithology, land cover, slope, aspect, elevation, curvature, topographic wetness index, and distances from faults, drainage networks, and roads. Furthermore, 70% of the landslides on the landslide inventory map were used for training, and the remaining 30% were used for validation. The RF-based model was validated using the area under the receiver operating characteristic (ROC) curve. Evaluation results indicated that the success and prediction rates of the model were 98.3% and 97.7%, respectively. Moreover, it was determined that incorrect land-use decisions, such as transforming forest areas into tea and hazelnut cultivation areas, induce the occurrence of landslides.

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GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China

Cited by 104 publications

References 84 publications

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

Mapping Post-Earthquake Landslide Susceptibility: A U-Net Like Approach

Geo-spatial approach-based landslide susceptibility mapping using analytical hierarchical process, frequency ratio, logistic regression and their ensemble methods

Random Forest-Based Landslide Susceptibility Mapping in Coastal Regions of Artvin, Turkey

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