GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models

Zhang, Ziwen; Yang, Fan; Han, Chuang; Wu, Yichu; Li, Tao; Li, Wenping; Wang, Qiqing; Liu, Ping

doi:10.1007/s12665-016-5732-0

Cited by 41 publications

(17 citation statements)

References 49 publications

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“…Various models including probabilistic approach (Chang et al 2014), rock engineering system and fuzzy C-means algorithm (Li et al 2017), empirical models (Kappes et al 2011;Horton et al 2013), artificial neural network (Chang and Chao 2006), logistic regression (LR) (Ayalew and Yamagishi 2005;Greco et al 2007), analytic hierarchy process (Yalcin 2008), clast distribution patterns (Faria Lima Lopes et al 2016), qualitative heuristic method, Flow-R (Blais-Stevens and Behnia 2016), and advanced Bayesian spatial models (Lombardo et al 2018) have been used for DFSM. Furthermore, the frequency ratio (FR) method has been proven to be effective, and it has been successfully applied to flash flood hazard susceptibility mapping and landslide susceptibility mapping (Cao et al 2016;Wang et al 2016;Zhang et al 2016). In view of the effectiveness of FR method, in the present study, this method was selected as the statistical method to better explore the effect of different mapping units on the susceptibility mapping of debris flow.…”

Section: Introductionmentioning

confidence: 99%

Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study

Qin

Cao

et al. 2019

Geomatics, Natural Hazards and Risk

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Debris flow susceptibility analysis is a prerequisite of risk assessment. The main objective of this study was to explore the accuracy and practicability of mapping units for evaluation of debris flow susceptibility. These units include grid cell units (GCUs), and watershed units (WUs) with the flow thresholds 10 000 (WU 10 000) and 5000 (WU 5000). The frequency ratio (FR) model was selected as the statistical method. Yongji County (YJC) of Jilin Province, China was selected as the research site, and a total of 123 debris flow disasters were surveyed. Eight influencing factors were considered and a total of three models were constructed. The predictive capabilities of the models were verified using an ROC curve and AUC. The results showed the three models to be accurate and the evaluation results of the GCU were found to be more accurate than others. However, when considering the effects of geology and geomorphology on the occurrence of debris flows, the WU was more feasible than the GCU. Therefore, the results indicate that the evaluation of debris flow susceptibility should be carried out based on the WU of the appropriate flow threshold in combination with the actual prevention and control of debris flow disasters.

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Section: Introductionmentioning

confidence: 99%

Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study

Qin

Cao

et al. 2019

Geomatics, Natural Hazards and Risk

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“…Statistical models are most commonly used in landslide susceptibility mapping, which are based on the analysis of the relationships between influencing factors and existing landslides [7]. In these statistical approaches, bivariate and multivariate statistical techniques are used for landslide susceptibility mapping throughout the world, including frequency ratio [8][9][10], index of entropy [11][12][13][14][15], bivariate statistical analysis [16], multivariate adaptive regression spline [17], analytical hierarchy process [18,19], statistical index [20,21], weight of evidence [13,21], evidential belief function [22,23], certainty factor [24,25], and logistic regression [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

et al. 2018

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Landslides cause a considerable amount of damage around the world every year. Landslide susceptibility assessments are useful for the mitigation of the associated potential risks to local economic development, land use planning, and decision makers. The main aim of this study was to present a novel hybrid approach of bagging (B)-based kernel logistic regression (KLR), named the BKLR model, for spatial prediction of landslides in the Shangnan County, China. We first selected 15 conditioning factors for landslide susceptibility modeling. Then, the prediction capability of all conditioning factors was evaluated using the least square support vector machine method. Model validation and comparison were performed based on the area under the receiver operating characteristic curve and several statistical-based indexes, including positive predictive rate, negative predictive rate, sensitivity, specificity, kappa index, and root mean square error. Results indicated that the BKLR ensemble model outperformed and outclassed the KLR and the benchmark support vector machine model. Our findings overall confirmed that a combination of the meta model with a decision tree classifier based on a functional algorithm can decrease the over-fitting and variance problems of data, which could enhance the prediction power of the landslide model. The resultant susceptibility maps could be useful for hazard mitigation in the study area and other similar landslide-prone areas.

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“…These methods are widely used in natural hazards mapping. Frequency ratio (FR) and statistical index (SI) models have been applied to landslide susceptibility mapping, and results found that these models are reasonably accurate and efficient [36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

et al. 2016

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This study focused on producing flash flood hazard susceptibility maps (FFHSM) using frequency ratio (FR) and statistical index (SI) models in the Xiqu Gully (XQG) of Beijing, China. First, a total of 85 flash flood hazard locations (n = 85) were surveyed in the field and plotted using geographic information system (GIS) software. Based on the flash flood hazard locations, a flood hazard inventory map was built. Seventy percent (n = 60) of the flooding hazard locations were randomly selected for building the models. The remaining 30% (n = 25) of the flooded hazard locations were used for validation. Considering that the XQG used to be a coal mining area, coalmine caves and subsidence caused by coal mining exist in this catchment, as well as many ground fissures. Thus, this study took the subsidence risk level into consideration for FFHSM. The ten conditioning parameters were elevation, slope, curvature, land use, geology, soil texture, subsidence risk area, stream power index (SPI), topographic wetness index (TWI), and short-term heavy rain. This study also tested different classification schemes for the values for each conditional parameter and checked their impacts on the results. The accuracy of the FFHSM was validated using area under the curve (AUC) analysis. Classification accuracies were 86.61%, 83.35%, and 78.52% using frequency ratio (FR)-natural breaks, statistical index (SI)-natural breaks and FR-manual classification schemes, respectively. Associated prediction accuracies were 83.69%, 81.22%, and 74.23%, respectively. It was found that FR modeling using a natural breaks classification method was more appropriate for generating FFHSM for the Xiqu Gully.

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GIS-based landslide susceptibility analysis using frequency ratio and evidential belief function models

Cited by 41 publications

References 49 publications

Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study

Mapping debris flow susceptibility based on watershed unit and grid cell unit: a comparison study

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

Flash Flood Hazard Susceptibility Mapping Using Frequency Ratio and Statistical Index Methods in Coalmine Subsidence Areas

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