A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China

Xie, Zhengtuan; Chen, Guan; Meng, Xiaohong; Zhang, Yi; Qiao, Lingling; Tan, Li

doi:10.1007/s12665-017-6640-7

Cited by 62 publications

(35 citation statements)

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“…into the training and testing datasets) may be another influential factor. This ratio was determined as 80:20 and 70:30 in the present study and (Xie et al 2017).…”

Section: Resultsmentioning

confidence: 63%

“…Furthermore, in comparison with the studies that used the same methods for the landslide susceptibility mapping in different areas, it was revealed that the main results of this study are more accurate. In Xie et al (2017), for instance, they used ANFIS-PSO, ANFIS-DE, and ANFIS-GA for landslide hazard assessment in Hanyuan County of China. In term of AUROC, the ANFIS-DE was found the best model with 0.844, followed by ANFIS-GA with 0.821, and ANFIS-PSO with 0.780.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, a total of 458 landslide points were identified within the Ludian County utilizing the previous recorded, aerial photos interpretation, and field monitoring using GPS in 1:25,000 scale (see Figure 1). Then, Similar to other studies (Wu et al 2014;Regmi et al 2016;Xie et al 2017;Moayedi et al 2019b), the landslide locations were randomly divided into two categories containing 366 landslides (80% of whole landslide locations) for training the proposed ensembles and 92 landslides (20% of whole landslide locations) for validating their performance. Remarkably, these points were assigned a value of '1'.…”

Section: Data Preparation and Spatial Interaction Between The Landslimentioning

confidence: 99%

“…Next, the same number of non-landslide points was randomly produced over the areas devoid of landslide. They were then divided into the training and testing samples as well, and were assigned a value of '0' (Xie et al 2017;Chen et al 2019b).…”

Section: Data Preparation and Spatial Interaction Between The Landslimentioning

confidence: 99%

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Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Moayedi

Mehrabi

Kalantar

et al. 2019

Geomatics, Natural Hazards and Risk

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Strong ground motions usually trigger lots of slope failures in the affected area. In this work, we analyse the occurrence likelihood of earthquake-triggered landslide by employing the ensembles of adaptive neuro-fuzzy inference systems (ANFIS) with four well-known metaheuristics techniques, namely particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), and differential evolution (DE) algorithms. Twelve landslide conditioning factors namely, elevation, slope degree, lithology, peak ground acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance to road, distance to river, distance to fault, normalized difference vegetation index (NDVI), slope aspect, and plan curvature are considered within the geographic information system (GIS) to produce the required spatial database. In this paper, frequency ratio (FR) model is used to evaluate the spatial interaction between the landslides and conditioning factors. Meantime, among a total of 458 marked earthquake-induced landslides, 366 (80%) are specified to the learning process, and the remaining 92 (20%) landslides are used to evaluate the accuracy of applied models. The landslide susceptibility maps are generated in the GIS environment. Three accuracy criteria of mean square error (MSE), root mean square error (RMSE), and area under the receiving operating characteristic curve (AUROC) are used to develop a ranking system for comparing the integrity of the designed models. The total ranking scores (TRSs) of 15, 8, 10, and 18, respectively, obtained for PSO-ANFIS, GA-ANFIS, ACO-ANFIS, and DE-ANFIS revealed the superiority of the DE algorithm compared to other metaheuristics techniques. Also, the DE-ANFIS emerged as the fastest ensemble, due to the highest convergence speed obtained for this model.

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“…into the training and testing datasets) may be another influential factor. This ratio was determined as 80:20 and 70:30 in the present study and (Xie et al 2017).…”

Section: Resultsmentioning

confidence: 63%

Section: Resultsmentioning

confidence: 99%

Section: Data Preparation and Spatial Interaction Between The Landslimentioning

confidence: 99%

Section: Data Preparation and Spatial Interaction Between The Landslimentioning

confidence: 99%

See 2 more Smart Citations

Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Moayedi

Mehrabi

Kalantar

et al. 2019

Geomatics, Natural Hazards and Risk

View full text Add to dashboard Cite

show abstract

“…Statistical approaches are quantitative methods which can use different functional relationship. They can be subdivided into: (1) physically-based methods [10,11]; and (2) traditional statistical methods, such as the frequency ratio [12,13], evidential belief function [14,15], weight of evidence [16,17], discriminant analysis [18][19][20][21], and logistic regression [22,23], (3) advanced data mining technologies, such as artificial neural networks [24,25], support vector machines [26][27][28], adaptive neuro-fuzzy inference systems [29,30], alternating decision trees [31], and functional trees [32,33]. These methods need landslide inventories expressed as landslide density maps to produce functional relationships with causative factors [34].…”

Section: Introductionmentioning

confidence: 99%

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Wang

Chen

2020

IJGI

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The areas where landslides occur frequently pose severe threats to the local population, which necessitates conducting regional landslide susceptibility mapping (LSM). In this study, four models including weight-of-evidence (WoE) and three WoE-based models, which were linear discriminant analysis (LDA), Fisher’s linear discriminant analysis (FLDA), and quadratic discriminant analysis (QDA), were used to obtain the LSM in the Nanchuan region of Chongqing, China. Firstly, a dataset was prepared from sixteen landslide causative factors, including eight topographic factors, three distance-related factors, and five environmental factors. A landslide inventory map including 298 landslide locations was also constructed and randomly divided with a ratio of 70:30 as training and validation data. Subsequently, the WoE method was used to estimate the relationship between landslides and the landslide causative factors, which assign a weight value to each class of causative factors. Finally, four models were applied using the training dataset, and the predictive performance of each model was compared using the validation datasets. The results showed that FLDA had a higher performance than the other three models according to the success rate curve (SRC) and prediction rate curve (PRC), illustrating that it could be considered a promising approach for landslide susceptibility mapping in the study area.

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Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

et al. 2023

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Landslides are dangerous events that threaten both human life and property. This research presents a case study of Kaghan valley catchment, which is an area of frequent landslide activity. We aim to present a comparison between the bivariate Landslide Numerical Risk Factor (LNRF), Statistical Index (SI) and Information Value (InfV) statistical models to evaluate landslide susceptibility. A total of 1556 landslides were identified using earlier reports, field surveys, and GOOGLE Earth imagery. The abundance of landslides is primarily controlled by acute deformation caused by a major thrust fault system and proximity to Hazara Kashmir Syntaxis (HKS). A landslide inventory was randomly partitioned into two datasets. 70% (1106) of landslides were used as a training phase of the models, whereas 30% (450) as validation of the three models. A spatial database of 11 conditioning factors was produced consisting of slope, aspect, elevation, lithology, land use, Topographic Wetness Index (TWI), rainfall, Stream Power Index (SPI), distance to faults, rivers and streams. All the landslide susceptibility assessment parameters were obtained from different sources and different landslide susceptibility maps were prepared on the GIS software. Performance of the three models was validated through the Receiver operator Characteristics (ROC) through success and prediction rate curves. Results show that the area under the ROC curve (AUC) for InfV, LNRF and SI models are 70.95, 83.99 and 67.56 for success rate curves and 70.75, 83.99 and 67.85 for prediction rate curves, respectively. LNRF having the highest AUC value proved to be superior for generating regional scale landslide susceptibility maps.

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A comparative study of landslide susceptibility mapping using weight of evidence, logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin, China

Cited by 62 publications

References 50 publications

Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with several metaheuristic algorithms for spatial susceptibility assessment of seismic-induced landslide

Spatial Prediction of Landslide Susceptibility Based on GIS and Discriminant Functions

Application of bivariate statistical techniques for landslide susceptibility mapping: A case study in Kaghan Valley, NW Pakistan

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