Regional Rainfall Warning System for Landslides with Creep Deformation in Three Gorges using a Statistical Black Box Model

Yan

et al. 2019

Applied Sciences

Self Cite

Landslides are one type of serious geological hazard which cause immense losses of local life and property. Landslide susceptibility prediction (LSP) can be used to determine the spatial probability of landslide occurrence in a certain area. It is important to implement LSP for landslide hazard prevention and reduction. This study developed a particle-swarm-optimized multilayer perceptron (PSO-MLP) model for LSP implementation to overcome the drawbacks of the conventional gradient descent algorithm and to determine the optimal structural parameters of MLP. Shicheng County in Jiangxi Province of China was used as the study area. In total, 369 landslides, randomly selected non-landslides, and 14 landslide-related predisposing factors were used to train and test the present PSO-MLP model and three other comparative models (an MLP-only model with the gradient descent algorithm, a back-propagation neural network (BPNN), and an information value (IV) model). The results showed that the PSO-MLP model had the most accurate prediction performance (area under the receiver operating characteristic curve (AUC) of 0.822 and frequency ratio (FR) accuracy of 0.856) compared with the MLP-only (0.791 and 0.829), BPNN (0.800 and 0.840), and IV (0.788 and 0.824) models. It can be concluded that the proposed PSO-MLP model addresses the drawbacks of the MLP-only model well and performs better than conventional artificial neural networks (ANNs) and statistical models. The spatial probability distribution law of landslide occurrence in Shicheng County was well revealed by the landslide susceptibility map produced using the PSO-MLP model. Furthermore, the present PSO-MLP model may have higher prediction and classification performances in some other fields compared with conventional ANNs and statistical models.

Section: Introductionmentioning

confidence: 99%

Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models

Yan

et al. 2019

Applied Sciences

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“…MNDWI is commonly used to reflect water information at the surface; the value of MNDWI in the region ranges from 0 to 1, and most of the collapses occur between 0.392 and 0.498 with the maximum FR (1.214) (Table 2). The distance to the river and MNDWI (Figure 4g,h) were used to characterize the influence of hydrological environment on collapse evolution [87,88].…”

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

et al. 2020

Remote Sensing

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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.

“…At a disintegration phenomenon time t ¼ 10 s, the first through crack occurs in the soil sample, with soil caving at both ends of the crack (Figure 3(b)). The reasons for this were listed as the following three points: (i) the effective stress in the soil sample decreases because the pores in the soil sample are filled with solution and the connections between the soil particles are weakened; (ii) soil swelling occurs in the soil sample after the solution infiltrated into the pores and cracks of the soil samples, which leads to the generation of fracture stress (Stoltz et al 2014;Guo et al 2019) and (iii) the stability of soil sample declines because of the wetting-drying cycles (Jiang et al 2018).…”

Section: Typical Disintegration Processes Of Soil Samplesmentioning

confidence: 99%

Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain

Liu

Song

Geomatics, Natural Hazards and Risk

2019

Self Cite

Granite residual soil has obvious disintegration characteristics, resulting in serious water and soil losses in South China. There is a lack of studies on the disintegration of granite residual soil. Therefore, it is important to determine the disintegration characteristics of granite residual soil, especially under combined influence of wetting-drying cycles and acid rain. Granite residual soil from Jinqiao Village, Yudou County of South China, was used as experimental material. The disintegration velocity was evaluated to investigate the effects of wetting-drying cycles and acid rain on soil disintegration characteristics. Under the pH conditions of acid rain, disintegration velocity increases as the number of wetting-drying cycles increases (from 0 to 4), reaches a maximum after four wetting-drying cycles; then remains relatively constant as the wetting-drying cycle number increases from 4 to 7. Meanwhile, under conditions of a given wetting-drying cycle number, disintegration velocity increases with the decrease in pH from 7 to 4, reaches a maximum at a pH of 4, and then remains relatively constant when the pH decreases from 4 to 1. Moreover, the disintegration velocity under the combined influence of wetting-drying cycles and acid rain is considerably higher than that under individual factor.