Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model

Huang, Faming; Cao, Zhongshan; Jiang, Shui‐Hua; Zhou, Chuangbing; Huang, Jingsong; Guo, Zhengfu

doi:10.1007/s10346-020-01473-9

Cited by 213 publications

(99 citation statements)

References 32 publications

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“…The RF algorithm can catch such nonlinear features among the factors but prevents overfitting [ 8 , 17 , 37 ]. Hence, the reliability of landslide susceptibility prediction mainly depends on the modeling approaches and the selection and processing of the available hazard-causative factors [ 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zhou

Lin

et al. 2021

IJERPH

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Landslides are one of the major geohazards threatening human society. The objective of this study was to conduct a landslide hazard susceptibility assessment for Ruijin, Jiangxi, China, and to provide technical support to the local government for implementing disaster reduction and prevention measures. Machine learning approaches, e.g., random forests (RFs) and support vector machines (SVMs) were employed and multiple geo-environmental factors such as land cover, NDVI, landform, rainfall, lithology, and proximity to faults, roads, and rivers, etc., were utilized to achieve our purposes. For categorical factors, three processing approaches were proposed: simple numerical labeling (SNL), weight assignment (WA)-based and frequency ratio (FR)-based. Then 19 geo-environmental factors were respectively converted into raster to constitute three 19-band datasets, i.e., DS1, DS2, and DS3 from three different processes. Then, 155 observed landslides that occurred in the past decades were vectorized, among which 70% were randomly selected to compose a training set (TS1) and the remaining 30% to form a validation set (VS1). A number of non-landslide (no-risk) samples distributed in the whole study area were identified in low slope (<1–3°) zones such as urban areas and croplands, and also added to the TS1 and VS1 in the same ratio. For comparison, we used the FR approach to identify the no-risk samples in both flat and non-flat areas, and merged them into the field-observed landslides to constitute another pair of training and validation sets (TS2 and VS2) using the same ratio of 7:3. The RF algorithm was applied to model the probability of the landslide occurrence using DS1, DS2, and DS3 as predictive variables and TS1 and TS2 for training to obtain the SNL-based, WA-based, and FR-based RF models, respectively. Verified against VS1 and VS2, the three models have similar overall accuracy (OA) and Kappa coefficient (KC), which are 89.61%, 91.47%, and 94.54%, and 0.7926, 0.8299, and 0.8908, respectively. All of them are much better than the three models obtained by SVM algorithm with OA of 81.79%, 82.86%, and 83%, and KC of 0.6337, 0.655, and 0.660. New case verification with the recent 26 landslide events of 2017–2020 revealed that the landslide susceptibility map from WA-based RF modeling was able to properly identify the high and very high susceptibility zones where 23 new landslides had occurred, and performed better than the SNL-based and FR-based RF modeling, though the latter has a slightly higher OA and KC. Hence, we concluded that all three RF models achieve reasonable risk prediction, but WA-based and FR-based RF modeling deserves a recommendation for application elsewhere. The results of this study may serve as reference for the local authorities in prevention and early warning of landslide hazards.

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Section: Introductionmentioning

confidence: 99%

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zhou

Lin

et al. 2021

IJERPH

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“…The collapse inventory and environmental factors in the study area were obtained as shown in Table 2, through in-depth analysis of various factors affecting the evolution of collapses. The data types of continuous environmental factors were divided into eight attribute levels using the natural break point method [80,81], and the aspect of the flat ground was separately divided into one class and set to −1, while discrete types such as lithology and distance to rivers were classified into four classes.…”

Section: Collapse-related Environmental Factor and Connection Resultsmentioning

confidence: 99%

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Fan

Huang

et al. 2020

Remote Sensing

Self Cite

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To study the uncertainties of a collapse susceptibility prediction (CSP) under the coupled conditions of different data-based models and different connection methods between collapses and environmental factors, An’yuan County in China with 108 collapses is used as the study case, and 11 environmental factors are acquired by data analysis of Landsat TM 8 and high-resolution aerial images, using a hydrological and topographical spatial analysis of Digital Elevation Modeling in ArcGIS 10.2 software. Accordingly, 20 coupled conditions are proposed for CSP with five different connection methods (Probability Statistics (PSs), Frequency Ratio (FR), Information Value (IV), Index of Entropy (IOE) and Weight of Evidence (WOE)) and four data-based models (Analytic Hierarchy Process (AHP), Multiple Linear Regression (MLR), C5.0 Decision Tree (C5.0 DT) and Random Forest (RF)). Finally, the CSP uncertainties are assessed using the area under receiver operation curve (AUC), mean value, standard deviation and significance test, respectively. Results show that: (1) the WOE-based models have the highest AUC accuracy, lowest mean values and average rank, and a relatively large standard deviation; the mean values and average rank of all the FR-, IV- and IOE-based models are relatively large with low standard deviations; meanwhile, the AUC accuracies of FR-, IV- and IOE-based models are consistent but higher than those of the PS-based model. Hence, the WOE exhibits a greater spatial correlation performance than the other four methods. (2) Among all the data-based models, the RF model has the highest AUC accuracy, lowest mean value and mean rank, and a relatively large standard deviation. The CSP performance of the RF model is followed by the C5.0 DT, MLR and AHP models, respectively. (3) Under the coupled conditions, the WOE-RF model has the highest AUC accuracy, a relatively low mean value and average rank, and a high standard deviation. The PS-AHP model is opposite to the WOE-RF model. (4) In addition, the coupled models show slightly better CSP performances than those of the single data-based models not considering connect methods. The CSP performance of the other models falls somewhere in between. It is concluded that the WOE-RF is the most appropriate coupled condition for CSP than the other models.

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“…e numerical model is presented in Figure 1. Based on practical experience and Saint-Venant's principle, the area affected by tunnel excavation is basically concentrated within 5 times the tunnel diameter [15][16][17]. Dimensions of the model used in this study were 100 m × 53 m × 70 m. e Mohr-Coulomb elastoplastic model was adopted for soil, and the shear expansion effect was neglected.…”

Section: Numerical Modelmentioning

confidence: 99%

Numerical Analysis on Reinforcement Range of a Closed Steel Sleeve against Collapse

Yin

2021

Advances in Civil Engineering

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Before the shield machine begins to excavate, the end of the station structure often requires extensive soil reinforcement to ensure construction safety. Closed steel sleeve can prevent water leakage, sand leakage, and cave door collapse by balancing the water and soil pressure on the tunnel surface, thereby reducing the reinforcement range. In this study, a launching project of a closed steel sleeve is investigated; the Madis GTS finite element analysis software is used to simulate the triple-tube high-pressure jet-grouting pile to reinforce the water-rich sand layer. Soil displacement and stress after opening of the tunnel door are studied in detail at different longitudinal reinforcement lengths and transverse reinforcement scopes. The results show that, as the longitudinal reinforcement length increases, the displacement of the soil shows a decreasing trend, and the greater the length of the reinforced soil, the smaller the reduction in displacement. Furthermore, with the decrease of the lateral reinforcement range, though the soil settlement area has increased, the displacement remains unchanged. However, changing the end reinforcement range has no effect on the soil stress. In general, based on the strength and stability of the soil after the gate is cut out, the reinforcement range of the closed steel sleeve can be appropriately reduced compared to traditional reinforcement methods.

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Landslide susceptibility prediction based on a semi-supervised multiple-layer perceptron model

Cited by 213 publications

References 32 publications

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Zonation of Landslide Susceptibility in Ruijin, Jiangxi, China

Uncertainties Analysis of Collapse Susceptibility Prediction Based on Remote Sensing and GIS: Influences of Different Data-Based Models and Connections between Collapses and Environmental Factors

Numerical Analysis on Reinforcement Range of a Closed Steel Sleeve against Collapse

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