Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Yang, Xin; Liu, Rui; Yang, Mei; Chen, Jingjue; Liu, Tianqiang; Yang, Yuantao; Chen, Wei; Wang, Yuting

doi:10.3390/rs13112166

Cited by 33 publications

(12 citation statements)

References 64 publications

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“…Normalized difference vegetation index (NDVI) was first proposed by Rouse et al in the 1970s [ 46 , 47 , 48 ]. Because NDVI can better reflect vegetation growth and vegetation coverage, it has been used by numerous scholars.…”

Section: Methodsmentioning

confidence: 99%

“…Because NDVI can better reflect vegetation growth and vegetation coverage, it has been used by numerous scholars. The larger the NDVI value, the larger the vegetation coverage [ 44 , 47 , 48 ]. In the present study, the MODIS data for 2019 with a spatial resolution of 250 m was selected, the vegetation coverage was extracted, and the outliers were removed using the ENVI5.3 software.…”

Section: Methodsmentioning

confidence: 99%

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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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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Deng

et al. 2022

IJERPH

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show abstract

“…In this tool, accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the prediction ability of the model. The calculation formula [6]…”

Section: Model Training and Performance Evaluation Of Svmmentioning

confidence: 99%

“…The occurrence of landslide disasters causes great losses to the economy and human life all over the world every year [1,2]. Natural events such as rainfall [3,4], earthquakes [5,6] and floods [7] often lead to a series of landslides. Landslide susceptibility mapping (LSM) is used to determine the probability of future landslides in the study area by comprehensively analyzing various topographic, geological and hydrological factors, as well as human activity, alongside historical landslide activity in the study area [8,9].…”

Section: Introductionmentioning

confidence: 99%

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Huang

Ding

et al. 2022

Remote Sensing

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Landslide susceptibility mapping (LSM) is an important element of landslide risk assessment, but the process often needs to span multiple platforms and the operation process is complex. This paper develops an efficient user-friendly toolbox including the whole process of LSM, known as the SVM-LSM toolbox. The toolbox realizes landslide susceptibility mapping based on a support vector machine (SVM), which can be integrated into the ArcGIS or ArcGIS Pro platform. The toolbox includes three sub-toolboxes, namely: (1) influence factor production, (2) factor selection and dataset production, and (3) model training and prediction. Influence factor production provides automatic calculation of DEM-related topographic factors, converts line vector data to continuous raster factors, and performs rainfall data processing. Factor selection uses the Pearson correlation coefficient (PCC) to calculate the correlations between factors, and the information gain ratio (IGR) to calculate the contributions of different factors to landslide occurrence. Dataset sample production includes the automatic generation of non-landslide data, data sample production and dataset split. The accuracy, precision, recall, F1 value, receiver operating characteristic (ROC) and area under curve (AUC) are used to evaluate the prediction ability of the model. In addition, two methods—single processing and multiprocessing—are used to generate LSM. The prediction efficiency of multiprocessing is much higher than that of the single process. In order to verify the performance and accuracy of the toolbox, Wuqi County, Yan’an City, Shaanxi Province was selected as the test area to generate LSM. The results show that the AUC value of the model is 0.8107. At the same time, the multiprocessing prediction tool improves the efficiency of the susceptibility prediction process by about 60%. The experimental results confirm the accuracy and practicability of the proposed toolbox in LSM.

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“…From the database, nine landslide mapping factors were selected based on the characteristics of landslide distribution in the study area and a review of relevant papers (Tang et al, 2020;Yang et al, 2021). It includes 1) topographic and geomorphological factors: slope, aspect, curvature, elevation, and relief; 2) the engineering rock group factor: engineering rock group; 3) the tectonic factor: fault density; 4) the hydrological factor: distance from the river; and 5) the surface cover factor: Land use.…”

Section: Mapping Factor Analysismentioning

confidence: 99%

Landslide susceptibility mapping based on the coupling of two correlation methods and the BP neural network model: A case study of the Baihetan Reservoir area, China

Xue

Feng²,

Li³

et al. 2022

Front. Environ. Sci.

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The correlation calculation model between landslide and mapping factors has a direct influence on the accuracy of landslide susceptibility mapping results. Using the Baihetan reservoir area as a case study, the effect of several correlation models on mapping landslide susceptibility is studied. The frequency ratio (FR) and the information value (IV) coupled BP neural network (BPNN) model was utilized to assess landslide susceptibility, with the mapping results of the single back propagation neural network (BPNN) model acting as a reference. The receiver operating characteristic (ROC) curve, the frequency ratio, and the susceptibility index distribution (mean value and standard deviation) are used to compare and assess landslide susceptibility values. The FR-BPNN coupling model is less precise than the IV-BPNN model. Findings from a single BPNN model for susceptibility mapping are less exact than those from a coupled model. Using the coupling model of the mapping factor correlation approach to assess landslide susceptibility has evident benefits, according to the study. The coupled model employing IV as the correlation method provides the most accurate and dependable susceptibility findings, and the mapping results are more consistent with the actual distribution of landslides in the study area. It can effectively direct disaster prevention efforts in the reservoir region.

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Incorporating Landslide Spatial Information and Correlated Features among Conditioning Factors for Landslide Susceptibility Mapping

Cited by 33 publications

References 64 publications

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

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

An Efficient User-Friendly Integration Tool for Landslide Susceptibility Mapping Based on Support Vector Machines: SVM-LSM Toolbox

Landslide susceptibility mapping based on the coupling of two correlation methods and the BP neural network model: A case study of the Baihetan Reservoir area, China

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