Landslide susceptibility evaluation based on GIS and information value model

Chen, Yumin; Ba, Qianqian; Wu, Qianjiao; Li, Xiangfei

doi:10.2991/nceece-15.2016.286

Cited by 4 publications

(5 citation statements)

References 4 publications

(4 reference statements)

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“…According to existing research [18,19,59] or Jenks natural breaks optimization, each landslide predisposing factor was divided into five classes, except for aspect, which was divided into nine classes. Using Equation (1), the information value of each class of landslide predisposing factor was calculated (Table 3).…”

Section: Application Of Ivmmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

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Landslides, as geological hazards, cause significant casualties and economic losses. Therefore, it is necessary to identify areas prone to landslides for prevention work. This paper proposes an improved information value model based on gray clustering (IVM-GC) for landslide susceptibility mapping. This method uses the information value derived from an information value model to achieve susceptibility classification and weight determination of landslide predisposing factors and, hence, obtain the landslide susceptibility of each study unit based on the clustering analysis. Using a landslide inventory of Chongqing, China, which contains 8435 landslides, three landslide susceptibility maps were generated based on the common information value model (IVM), an information value model improved by an analytic hierarchy process (IVM-AHP) and our new improved model. Approximately 70% (5905) of the inventory landslides were used to generate the susceptibility maps, while the remaining 30% (2530) were used to validate the results. The training accuracies of the IVM, IVM-AHP and IVM-GC were 81.8%, 78.7% and 85.2%, respectively, and the prediction accuracies were 82.0%, 78.7% and 85.4%, respectively. The results demonstrate that all three methods perform well in evaluating landslide susceptibility. Among them, IVM-GC has the best performance.

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Section: Application Of Ivmmentioning

confidence: 99%

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Chen

Deng³

et al. 2017

IJGI

Self Cite

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“…(3) Slope orientation: When vegetation coverage is equal, the sunny slope exhibits ample water and heat, leading to the saturation of internal water within the rock mass. This saturation, coupled with water infiltration, results in lower initiation conditions for geological hazards, increasing the likelihood of their occurrence [16]. Utilizing the surface analysis function of ArcGIS 10.2 software, the slope aspect information of the study area was extracted from the DEM data, and it was divided into north, northeast, east, southeast, south, southwest, west, and northwest (Figure 2c).…”

Section: Susceptibility Evaluation 41 Selection and Grading Of The Ev...mentioning

confidence: 99%

Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China

et al. 2023

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In China, the majority of mountainous regions are characterized by complex topography and a delicate, sensitive geological environment. These areas, which exhibit insufficient infrastructure and widespread irrational human engineering activities, are often susceptible to geological hazards such as slope instability and soil mass movements. These geological hazards pose substantial threats to human lives and property, hindering the progress of mountainous areas. Therefore, conducting research on evaluating the vulnerability of slope rock and soil mass movement geological hazards (hereinafter referred to as geological hazards) is of utmost importance for hazard prevention, emergency management, and economic advancement in these regions. This study focuses on Xuanwei City and selects eight factors for evaluation, including elevation, gradient, slope aspect, normalized vegetation index, stratigraphic lithology, distance from faults, distance from rivers, and distance from roads. These factors are chosen based on a comprehensive analysis of the spatial and temporal distribution of geological hazards and hazard incubation conditions. Two paired models, the deterministic coefficient model + logistic regression model (CF+LR) and the information quantity model + logistic regression model (I+LR), were employed to assess the study area quantitatively. The performance of these models was assessed by employing receiver operating characteristic (ROC) curves and calculating the corresponding area under curve (AUC) values. The results indicate that: (1) The AUC values for the coupled CF+LR and I+LR models are 0.799 and 0.772, respectively. These results indicate that both models provide an objective and reliable assessment of the vulnerability to geological hazards, specifically slope rock and soil mass movements, in the study area. (2) Based on the CF+LR model calculations, the geological hazard susceptibility of Xuanwei City can be categorized into four zones: extremely high susceptibility (6.09%), high susceptibility (31.08%), medium susceptibility (32.26%), and low susceptibility (30.57%). (3) The CF+LR model more accurately represents the evaluation results and offers a strong reference value.

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“…Major advances in computing power, remote sensing, and geographic information systems (GIS) have facilitated the development of landslide susceptibility maps. Various landslide susceptibility mapping models and methods have been proposed, including: (1) knowledge-based approaches such as analytic hierarchy process [ 14 ] and expert scoring method [ 15 ]; (2) data-driven approaches such as information content method [ 16 ], frequency ratio [ 17 ], certainty factor [ 18 ], index of entropy [ 19 ], and logistic regression analysis [ 20 ]; and (3) machine-learning methods (ML), such as decision tree [ 21 , 22 ], artificial neural network (ANN) [ 23 ], support vector machine (SVM) [ 24 ], and random forest [ 25 ]. The combination of GIS with data-driven methods or machine learning methods (ML) has been widely used for landslide susceptibility assessment using spatial and non-spatial data [ 17 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, Cheng, Y. et al [ 26 ] used analytic hierarchy process to study highway tunnel landslides. Chen, F. et al [ 16 ] used the information model for spatial susceptibility prediction and analysis of landslides. Ali, S. et al [ 27 ] used GIS technology to draw the landslide susceptibility map along the China–Pakistan Economic Corridor (Karakoram Highway).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

Deng

et al. 2022

IJERPH

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At present, landslide susceptibility assessment (LSA) based on landslide characteristics in different areas is an effective measure for landslide management. Nujiang Prefecture in China has steep mountain slopes, a large amount of water and loose soil, and frequent landslide disasters, which have caused a large number of casualties and economic losses. This paper aims to understand the characteristics and formation mechanism of regional landslides through the evaluation of landslide susceptibility so as to provide relevant references and suggestions for spatial planning and disaster prevention and mitigation in Nujiang Prefecture. Based on the grid cell, this study selected 10 parameters, namely elevation, slope, aspect, lithology, proximity to faults, proximity to road, proximity to rivers, normalized difference vegetation index (NDVI), land-use type, and precipitation. Support vector machine (SVM), certainty factor method (CF), and deterministic coefficient method–support vector machine (CF-SVM) were used to evaluate the landslide susceptibility in Nujiang Prefecture. According to these three models, the study area was divided into five landslide susceptibility grades, including extremely high susceptibility, high susceptibility, moderate susceptibility, low susceptibility, and very low susceptibility. Receiver operating characteristic curve (ROC) was applied to verify the accuracy of the model. The results showed that CF model (ROC = 0.865), SVM model (ROC = 0.892), CF-SVM model (ROC = 0.925), and CF-SVM model showed better performance. Therefore, CF-SVM model results were selected for analysis. The study found that the characteristics of high and extremely high landslide-prone areas in Nujiang Prefecture have the following characteristics: intense human activities, large density of buildings and arable land, rich water resources, good economic development, perfect transportation facilities, and complex topography and landform. In addition, there is a finding inconsistent with our common sense that the distribution of landslide disasters in the study area does not decrease with the increase of NDVI value. This is because the Nujiang River basin is a high mountain canyon area with low rock strength, barren soil, and underdeveloped vegetation and root system. In an area with large slope, the probability of landslide disaster will increase with the increase of NDVI. The CF-SVM coupling model adopted in this study is a good first attempt in the study of landslide hazard susceptibility in Nujiang Prefecture.

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Landslide susceptibility evaluation based on GIS and information value model

Cited by 4 publications

References 4 publications

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

An Improved Information Value Model Based on Gray Clustering for Landslide Susceptibility Mapping

Slope Rock and Soil Mass Movement Geological Hazards Susceptibility Evaluation Using Information Quantity, Deterministic Coefficient, and Logistic Regression Models and Their Comparison at Xuanwei, China

Evaluation of Landslide Susceptibility Based on CF-SVM in Nujiang Prefecture

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