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Faming Huang
School of Civil Engineering and Architecture, Nanchang University, Nanchang 330031, China

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Journal article
Published: 20 March 2021 in ISPRS International Journal of Geo-Information
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Dam deformation monitoring can directly identify the safe operation state of a dam in advance, which plays an important role in dam safety management. Three-dimensional (3D) terrestrial laser scanning technology is widely used in the field of deformation monitoring due to its fast, complete, and high-density 3D data acquisition capabilities. However, 3D point clouds are characterized by rough surfaces, discrete distributions, which affect the accuracy of deformation analysis of two states data. In addition, it is impossible to directly extract the correspondence points from an irregularly distributed point cloud to unify the coordinates of the two states’ data, and the correspondence lines and planes are often difficult to obtain in the natural environment. To solve the above problems, this paper studies a displacement change detection method for arch dams based on two-step point cloud registration and contour model comparison method. In the environment around a dam, the stable rock is used as the correspondence element to improve the registration accuracy, and a two-step registration method from rough to fine using the iterative closest point algorithm is present to describe the coordinate unification of the two states’ data without control network and target. Then, to analyze the displacement variation of an arch dam surface in two states and improve the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud, the contour model fitting the point clouds is used to compare the change in distance between models. Finally, the method of this paper is applied to the Xiahuikeng Arch Dam, and the displacement changes of the entire dam in different periods are visualized by comparing with the existing methods. The results show that the displacement change in the middle area of the dam is generally greater than that of the two banks, increasing with the increase in elevation, which is consistent with the displacement change behavior of the arch dam during operation and can reach millimeter-level accuracy.

ACS Style

Yijing Li; Ping Liu; HuoKun Li; Faming Huang. A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams. ISPRS International Journal of Geo-Information 2021, 10, 184 .

AMA Style

Yijing Li, Ping Liu, HuoKun Li, Faming Huang. A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams. ISPRS International Journal of Geo-Information. 2021; 10 (3):184.

Chicago/Turabian Style

Yijing Li; Ping Liu; HuoKun Li; Faming Huang. 2021. "A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams." ISPRS International Journal of Geo-Information 10, no. 3: 184.

Journal article
Published: 04 March 2021 in CATENA
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This paper aims to explore the influences of different attribute interval numbers (AINs) in the frequency ratio (FR) analysis of continuous environmental factors and the influences of different data-based models on the uncertainties of landslide susceptibility prediction (LSP). Taking Ningdu County of China as study area, 446 landslides and nine environmental factors are first acquired. Then the FR values of environmental factors under 6 different AINs (4, 6, 8, 12, 16 and 20) and 6 different data-based models (FR model, grey relational degree (GRD), logistic regression (LR), multilayer perceptron (MLP), C5.0 decision tree (C5.0 DT) and random forest (RF)) are set to 36 different conditions. Finally, the LSP results with uncertainties under all conditions are discussed. Results show that: 1) For a certain model, the LSP accuracy gradually increases with the AINs increasing from 4 to 8, and then the increase rate decreases until the accuracy is stable with the AINs increasing from 8 to 20; 2) For a certain AIN, the LSP accuracy of RF is higher than that of C5.0 DT, followed by the MLP, LR, FR and GRD; 3) The LSP accuracy is highest under an AIN of 20 and RF and is satisfied under an AIN of 8 and RF, while is the lowest under an AIN of 4 and GRD; 4) The landslide susceptibility indexes (LSIs) under AINs of 4, 6 and 12 are significantly different from the other AINs, and the LSIs calculated by the C5.0 DT and RF are significantly different compared to the other models; 5) The mean values and standard deviations of LSIs calculated by the MLP, C5.0 DT and RF models are relatively smaller and larger, respectively, than those of the other models, indicating that the LSIs calculated by these models are more consistent with the actual landslide distribution features.

ACS Style

Faming Huang; Zhou Ye; Shui-Hua Jiang; Jinsong Huang; Zhilu Chang; Jiawu Chen. Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. CATENA 2021, 202, 105250 .

AMA Style

Faming Huang, Zhou Ye, Shui-Hua Jiang, Jinsong Huang, Zhilu Chang, Jiawu Chen. Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models. CATENA. 2021; 202 ():105250.

Chicago/Turabian Style

Faming Huang; Zhou Ye; Shui-Hua Jiang; Jinsong Huang; Zhilu Chang; Jiawu Chen. 2021. "Uncertainty study of landslide susceptibility prediction considering the different attribute interval numbers of environmental factors and different data-based models." CATENA 202, no. : 105250.

Journal article
Published: 08 February 2021 in IEEE Geoscience and Remote Sensing Letters
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Landslide susceptibility prediction (LSP) is a useful technology for landslide prevention. Due to the complex nonlinear correlations among environmental factors, traditional machine learning (ML) models have unsatisfactory LSP accuracies. In this letter, a sparse feature extraction network (SFE+) is proposed for LSP. First, the landslides and environmental factors are collected, and frequency ratios of environmental factors are calculated as the model inputs. Second, the input data are passed through the input layer with the dropout, and then, the features are passed through the hidden layers, that is, the k% lifetime sparsity layers. The hidden layers are employed to further sparse these factors to obtain the independent and redundant prediction features as much as possible. Finally, certain classifiers are used to realize the LSP in the study area. SFE-support vector machine (SVM), SFE-logistic regression (LR), and SFE-stochastic gradient descent (SGD) models are built. For comparison, principal component analysis (PCA)-SVM, PCA-LR, PCA-SGD, SVM, LR, and SGD models are also built for LSP in Shicheng County, China. Results show that the SFE-based ML models, especially the SFE-SVM, can effectively extract the sparse nonlinear features of environmental factors to improve LSP accuracies and have promising prospects for LSP.

ACS Style

Li Zhu; Gongjian Wang; Faming Huang; Yan Li; Wei Chen; Haoyuan Hong. Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.

AMA Style

Li Zhu, Gongjian Wang, Faming Huang, Yan Li, Wei Chen, Haoyuan Hong. Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.

Chicago/Turabian Style

Li Zhu; Gongjian Wang; Faming Huang; Yan Li; Wei Chen; Haoyuan Hong. 2021. "Landslide Susceptibility Prediction Using Sparse Feature Extraction and Machine Learning Models Based on GIS and Remote Sensing." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.

Journal article
Published: 17 December 2020 in 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.

ACS Style

Wenbin Li; Xuanmei Fan; Faming Huang; Wei Chen; Haoyuan Hong; Jinsong Huang; Zizheng Guo. 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. Remote Sensing 2020, 12, 4134 .

AMA Style

Wenbin Li, Xuanmei Fan, Faming Huang, Wei Chen, Haoyuan Hong, Jinsong Huang, Zizheng Guo. 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. Remote Sensing. 2020; 12 (24):4134.

Chicago/Turabian Style

Wenbin Li; Xuanmei Fan; Faming Huang; Wei Chen; Haoyuan Hong; Jinsong Huang; Zizheng Guo. 2020. "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." Remote Sensing 12, no. 24: 4134.

Journal article
Published: 08 September 2020 in ISPRS International Journal of Geo-Information
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Regional terrain complexity assessment (TCA) is an important theoretical foundation for geological feature identification, hydrological information extraction and land resources utilization. However, the previous TCA models have many disadvantages; for example, comprehensive consideration and redundancy information analysis of terrain factors is lacking, and the terrain complexity index is difficult to quantify. To overcome these drawbacks, a TCA model based on principal component analysis (PCA) and a geographic information system (GIS) is proposed. Taking Jiangxi province of China as an example, firstly, ten terrain factors are extracted using a digital elevation model (DEM) in GIS software. Secondly, PCA is used to analyze the information redundancy of these terrain factors and deal with data compression. Then, the comprehensive evaluation of the compressed terrain factors is conducted to obtain quantitative terrain complexity indexes and a terrain complexity map (TCM). Finally, the TCM produced by the PCA method is compared with those produced by the slope-only, the variation coefficient and K-means clustering models based on the topographic map drawn by the Bureau of Land and Resources of Jiangxi province. Meanwhile, the TCM is also verified by the actual three-dimensional aerial images. Results show that the correlation coefficients between the TCMs produced by the PCA, slope-only, variable coefficient and K-means clustering models and the local topographic map are 0.894, 0.763, 0.816 and 0.788, respectively. It is concluded that the TCM of the PCA method matches well with the actual field terrain features, and the PCA method can reflect the regional terrain complexity characteristics more comprehensively and accurately when compared to the other three methods.

ACS Style

Faming Huang; Jianbo Yang; Biao Zhang; Yijing Li; Jinsong Huang; Na Chen. Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China. ISPRS International Journal of Geo-Information 2020, 9, 539 .

AMA Style

Faming Huang, Jianbo Yang, Biao Zhang, Yijing Li, Jinsong Huang, Na Chen. Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China. ISPRS International Journal of Geo-Information. 2020; 9 (9):539.

Chicago/Turabian Style

Faming Huang; Jianbo Yang; Biao Zhang; Yijing Li; Jinsong Huang; Na Chen. 2020. "Regional Terrain Complexity Assessment Based on Principal Component Analysis and Geographic Information System: A Case of Jiangxi Province, China." ISPRS International Journal of Geo-Information 9, no. 9: 539.

Original paper
Published: 29 June 2020 in Bulletin of Engineering Geology and the Environment
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The Revised Universal Soil Loss Equation (RUSLE) models are most widely used for quantitative prediction of soil erosion. However, these models have many shortcomings. For example, the annual total rainfall is often adopted, ignoring the inhomogeneity of seasonal rainfall. The adopted vegetation coverage indexes (VCIs) are usually the annual average vegetation coverage or VCIs obtained by monitoring on a specific day, ignoring the seasonal changes in VCIs during the year. In addition, the impact of slope on the conservation practices factor is not considered. To overcome these problem, this study aims to propose a seasonal and slope factor-based RUSLE (SUSLE) model that considers the seasonal changes in rainfall and VCIs and the effect of slope on the conservation practices factor. Based on GIS and remote sensing, the quantitative prediction of soil erosion in Ningdu County, Jiangxi Province, in 2017 is taken as a case study. The traditional RUSLE model and the proposed SUSLE model are analyzed and compared. Results show that the overall distribution characteristics of soil erosion in the two models are similar that the SUSLE model is more consistent than the RUSLE model in all erosion levels and that the prediction performances of the SUSLE model in the very low, moderate, and high erosion levels are better than those of the RUSLE model. The distribution characteristics of soil erosion in different periods and the relationships between soil erosion and environmental factors (e.g., slope and land use) under the SUSLE model are discussed. The results show that the maximum erosion area occurred in spring and the minimum area in autumn; the soil erosion amount on slopes of 8~25° reached 65.14% of the total amount; bare grassland and cultivated land are the main land cover types impacted by soil erosion in Ningdu County.

ACS Style

Faming Huang; Jiawu Chen; Chi Yao; Zhilu Chang; Qinghui Jiang; Shu Li; Zizheng Guo. SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion. Bulletin of Engineering Geology and the Environment 2020, 79, 5213 -5228.

AMA Style

Faming Huang, Jiawu Chen, Chi Yao, Zhilu Chang, Qinghui Jiang, Shu Li, Zizheng Guo. SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion. Bulletin of Engineering Geology and the Environment. 2020; 79 (10):5213-5228.

Chicago/Turabian Style

Faming Huang; Jiawu Chen; Chi Yao; Zhilu Chang; Qinghui Jiang; Shu Li; Zizheng Guo. 2020. "SUSLE: a slope and seasonal rainfall-based RUSLE model for regional quantitative prediction of soil erosion." Bulletin of Engineering Geology and the Environment 79, no. 10: 5213-5228.

Journal article
Published: 08 June 2020 in ISPRS International Journal of Geo-Information
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Soil erosion (SE) provides slide mass sources for landslide formation, and reflects long-term rainfall erosion destruction of landslides. Therefore, it is possible to obtain more reliable landslide susceptibility prediction results by introducing SE as a geology and hydrology-related predisposing factor. The Ningdu County of China is taken as a research area. Firstly, 446 landslides are obtained through government disaster survey reports. Secondly, the SE amount in Ningdu County is calculated and nine other conventional predisposing factors are obtained under both 30 m and 60 m grid resolutions to determine the effects of SE on landslide susceptibility prediction. Thirdly, four types of machine-learning predictors with 30 m and 60 m grid resolutions—C5.0 decision tree (C5.0 DT), logistic regression (LR), multilayer perceptron (MLP) and support vector machine (SVM)—are applied to construct the landslide susceptibility prediction models considering the SE factor as SE-C5.0 DT, SE-LR, SE-MLP and SE-SVM models; C5.0 DT, LR, MLP and SVM models with no SE are also used for comparisons. Finally, the area under receiver operating feature curve is used to verify the prediction accuracy of these models, and the relative importance of all the 10 predisposing factors is ranked. The results indicate that: (1) SE factor plays the most important role in landslide susceptibility prediction among all 10 predisposing factors under both 30 m and 60 m resolutions; (2) the SE-based models have more accurate landslide susceptibility prediction than the single models with no SE factor; (3) all the models with 30 m resolutions have higher landslide susceptibility prediction accuracy than those with 60 m resolutions; and (4) the C5.0 DT and SVM models show higher landslide susceptibility prediction performance than the MLP and LR models.

ACS Style

Faming Huang; Jiawu Chen; Zhen Du; Chi Yao; Jinsong Huang; Qinghui Jiang; Zhilu Chang; Shu Li. Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models. ISPRS International Journal of Geo-Information 2020, 9, 377 .

AMA Style

Faming Huang, Jiawu Chen, Zhen Du, Chi Yao, Jinsong Huang, Qinghui Jiang, Zhilu Chang, Shu Li. Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models. ISPRS International Journal of Geo-Information. 2020; 9 (6):377.

Chicago/Turabian Style

Faming Huang; Jiawu Chen; Zhen Du; Chi Yao; Jinsong Huang; Qinghui Jiang; Zhilu Chang; Shu Li. 2020. "Landslide Susceptibility Prediction Considering Regional Soil Erosion Based on Machine-Learning Models." ISPRS International Journal of Geo-Information 9, no. 6: 377.

Journal article
Published: 12 March 2020 in Sensors
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Landslide susceptibility prediction (LSP) modeling is an important and challenging problem. Landslide features are generally uncorrelated or nonlinearly correlated, resulting in limited LSP performance when leveraging conventional machine learning models. In this study, a deep-learning-based model using the long short-term memory (LSTM) recurrent neural network and conditional random field (CRF) in cascade-parallel form was proposed for making LSPs based on remote sensing (RS) images and a geographic information system (GIS). The RS images are the main data sources of landslide-related environmental factors, and a GIS is used to analyze, store, and display spatial big data. The cascade-parallel LSTM-CRF consists of frequency ratio values of environmental factors in the input layers, cascade-parallel LSTM for feature extraction in the hidden layers, and cascade-parallel full connection for classification and CRF for landslide/non-landslide state modeling in the output layers. The cascade-parallel form of LSTM can extract features from different layers and merge them into concrete features. The CRF is used to calculate the energy relationship between two grid points, and the extracted features are further smoothed and optimized. As a case study, the cascade-parallel LSTM-CRF was applied to Shicheng County of Jiangxi Province in China. A total of 2709 landslide grid cells were recorded and 2709 non-landslide grid cells were randomly selected from the study area. The results show that, compared with existing main traditional machine learning algorithms, such as multilayer perception, logistic regression, and decision tree, the proposed cascade-parallel LSTM-CRF had a higher landslide prediction rate (positive predictive rate: 72.44%, negative predictive rate: 80%, total predictive rate: 75.67%). In conclusion, the proposed cascade-parallel LSTM-CRF is a novel data-driven deep learning model that overcomes the limitations of traditional machine learning algorithms and achieves promising results for making LSPs.

ACS Style

Li Zhu; Lianghao Huang; Linyu Fan; Jinsong Huang; Faming Huang; Jiawu Chen; Zihe Zhang; Yuhao Wang. Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network. Sensors 2020, 20, 1576 .

AMA Style

Li Zhu, Lianghao Huang, Linyu Fan, Jinsong Huang, Faming Huang, Jiawu Chen, Zihe Zhang, Yuhao Wang. Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network. Sensors. 2020; 20 (6):1576.

Chicago/Turabian Style

Li Zhu; Lianghao Huang; Linyu Fan; Jinsong Huang; Faming Huang; Jiawu Chen; Zihe Zhang; Yuhao Wang. 2020. "Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network." Sensors 20, no. 6: 1576.

Journal article
Published: 04 February 2020 in Remote Sensing
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Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.

ACS Style

Zhilu Chang; Zhen Du; Fan Zhang; Faming Huang; Jiawu Chen; Wenbin Li; Zizheng Guo. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sensing 2020, 12, 502 .

AMA Style

Zhilu Chang, Zhen Du, Fan Zhang, Faming Huang, Jiawu Chen, Wenbin Li, Zizheng Guo. Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sensing. 2020; 12 (3):502.

Chicago/Turabian Style

Zhilu Chang; Zhen Du; Fan Zhang; Faming Huang; Jiawu Chen; Wenbin Li; Zizheng Guo. 2020. "Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models." Remote Sensing 12, no. 3: 502.

Journal article
Published: 04 September 2019 in Applied Sciences
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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.

ACS Style

Deying Li; Faming Huang; Liangxuan Yan; Zhongshan Cao; Jiawu Chen; Zhou Ye. Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models. Applied Sciences 2019, 9, 3664 .

AMA Style

Deying Li, Faming Huang, Liangxuan Yan, Zhongshan Cao, Jiawu Chen, Zhou Ye. Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models. Applied Sciences. 2019; 9 (18):3664.

Chicago/Turabian Style

Deying Li; Faming Huang; Liangxuan Yan; Zhongshan Cao; Jiawu Chen; Zhou Ye. 2019. "Landslide Susceptibility Prediction Using Particle-Swarm-Optimized Multilayer Perceptron: Comparisons with Multilayer-Perceptron-Only, BP Neural Network, and Information Value Models." Applied Sciences 9, no. 18: 3664.

Journal article
Published: 22 June 2019 in Applied Mathematical Modelling
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A soil water retention curve is one of the fundamental elements used to describe unsaturated soil. The accurate determination of soil water retention curve requires sufficient available information. However, the amount of measurement data is generally limited due to the restriction of time or test apparatus. As a result, it is a challenge to determine the soil water retention curve from limited measurement data. To address this problem, a Bayesian framework is proposed. In the Bayesian framework, Bayesian updating can be employed using the posterior distribution that is obtained by the Markov chain Monte Carlo sampling method with the Delayed Rejection Adaptive Metropolis algorithm. The parameters of soil water retention curve model are represented by the sample statistics of updating posterior distribution. A new updating algorithm based on Bayesian framework is proposed to predict the soil water retention curve using the ideal data and the limited measurement data of the granite residual soil and sand. The results show that the proposed prediction algorithm exhibits an excellent capability for more accurately determining the soil water retention curve with limited measured data. The uncertainty of updating parameters and the influence of the prior knowledge can be reduced. The converged results can be derived using the proposed prediction algorithm even if the prior knowledge is incomplete.

ACS Style

Weiping Liu; Xiaoyan Luo; Faming Huang; Mingfu Fu. Prediction of soil water retention curve using Bayesian updating from limited measurement data. Applied Mathematical Modelling 2019, 76, 380 -395.

AMA Style

Weiping Liu, Xiaoyan Luo, Faming Huang, Mingfu Fu. Prediction of soil water retention curve using Bayesian updating from limited measurement data. Applied Mathematical Modelling. 2019; 76 ():380-395.

Chicago/Turabian Style

Weiping Liu; Xiaoyan Luo; Faming Huang; Mingfu Fu. 2019. "Prediction of soil water retention curve using Bayesian updating from limited measurement data." Applied Mathematical Modelling 76, no. : 380-395.

Original paper
Published: 15 April 2019 in Bulletin of Engineering Geology and the Environment
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Soil erosion leads to soil loss and affects the hydraulic and mechanical properties of soils, and as a result, the soil fertility, eco-environmental quality, and reliability of engineering facilities will decline. Thus, it is very significant to study the mechanisms of subsurface erosion of granitic under different natural environmental conditions. However, limited studies focus on the effects of combined erosion angles and flow discharges on the subsurface erosion amount. This study self-made a subsurface erosion simulator and designed a series of combinational conditions of different soil column angles (0°, 30°, 60°) and different flow discharges (25 l/h, 50 l/h, 100 l/h) to simulate the subsurface erosion phenomenon and processes of moisture migration within granitic collected from a collapsing erosion area in southern China. Results show that the subsurface erosion development in the soil column is a complex and progressive process with obvious preferential flow, which indicates the transportability of soil particles, and the processes of soil subsurface erosion change markedly along with the change of soil column angle and flow discharge. Moreover, the growth rates of wetting front and subsurface erosion amount will speed up along with the increase of soil column angle of flow discharge. The relations between the advance rate of wetting front and time since the beginning of the test demonstrate bilinear. The erosion amount has obvious fluctuation during the process of subsurface erosion in granitic due to fine particles erosion, reposition, pore clogging, and flushing.

ACS Style

Weiping Liu; Shaofeng Wan; Faming Huang; Xiaoyan Luo; Mingfu Fu. Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges. Bulletin of Engineering Geology and the Environment 2019, 78, 5877 -5888.

AMA Style

Weiping Liu, Shaofeng Wan, Faming Huang, Xiaoyan Luo, Mingfu Fu. Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges. Bulletin of Engineering Geology and the Environment. 2019; 78 (8):5877-5888.

Chicago/Turabian Style

Weiping Liu; Shaofeng Wan; Faming Huang; Xiaoyan Luo; Mingfu Fu. 2019. "Experimental study of subsurface erosion in granitic under the conditions of different soil column angles and flow discharges." Bulletin of Engineering Geology and the Environment 78, no. 8: 5877-5888.

Review
Published: 01 January 2019 in Geomatics, Natural Hazards and Risk
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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.

ACS Style

Weiping Liu; Xinqiang Song; Faming Huang; Lina Hu. Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain. Geomatics, Natural Hazards and Risk 2019, 10, 1912 -1927.

AMA Style

Weiping Liu, Xinqiang Song, Faming Huang, Lina Hu. Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain. Geomatics, Natural Hazards and Risk. 2019; 10 (1):1912-1927.

Chicago/Turabian Style

Weiping Liu; Xinqiang Song; Faming Huang; Lina Hu. 2019. "Experimental study on the disintegration of granite residual soil under the combined influence of wetting–drying cycles and acid rain." Geomatics, Natural Hazards and Risk 10, no. 1: 1912-1927.

Journal article
Published: 01 November 2018 in Applied Mathematical Modelling
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Conditional random field model can make best use of limited site investigation data to properly characterize the spatial variation of soil properties. This paper aims to propose a simplified approach for generating conditional random fields of soil undrained shear strength. A numerical method is adopted to validate the effectiveness of the proposed approach. With the proposed approach, the analytical posterior statistics of spatially varying undrained shear strength conditioned on the known values at measurement locations can be obtained. The conditional random field model of undrained shear strength is constructed using the field vane shear test data at a site of the west side highway in New York and the probability of slope failure is estimated by subset simulation. A clay slope under undrained conditions is investigated as an example to illustrate the proposed approach. The effects of borehole location and borehole layout scheme on the slope reliability are addressed. The results indicate that the proposed approach not only can well incorporate the limited site investigation data into modelling of the actual spatial variation of soil parameters by conditional random fields, but also can capture the depth-dependent nature of soil properties. The realizations of conditional random fields generated by the proposed approach can be well constrained to the site investigation data.

ACS Style

Shui-Hua Jiang; Jinsong Huang; Faming Huang; Jianhua Yang; Chi Yao; Chuang-Bing Zhou. Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis. Applied Mathematical Modelling 2018, 63, 374 -389.

AMA Style

Shui-Hua Jiang, Jinsong Huang, Faming Huang, Jianhua Yang, Chi Yao, Chuang-Bing Zhou. Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis. Applied Mathematical Modelling. 2018; 63 ():374-389.

Chicago/Turabian Style

Shui-Hua Jiang; Jinsong Huang; Faming Huang; Jianhua Yang; Chi Yao; Chuang-Bing Zhou. 2018. "Modelling of spatial variability of soil undrained shear strength by conditional random fields for slope reliability analysis." Applied Mathematical Modelling 63, no. : 374-389.

Conference paper
Published: 01 January 2018 in Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management
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ACS Style

Shui-Hua Jiang; Jinsong Huang; Faming Huang. An Analytical Conditional Random field Sampling Approach for Slope Reliability Analysis. Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management 2018, 1 .

AMA Style

Shui-Hua Jiang, Jinsong Huang, Faming Huang. An Analytical Conditional Random field Sampling Approach for Slope Reliability Analysis. Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management. 2018; ():1.

Chicago/Turabian Style

Shui-Hua Jiang; Jinsong Huang; Faming Huang. 2018. "An Analytical Conditional Random field Sampling Approach for Slope Reliability Analysis." Proceedings of the 6th International Symposium on Reliability Engineering and Risk Management , no. : 1.

Journal article
Published: 01 June 2017 in Engineering Geology
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Among the machine learning models used for landslide susceptibility indexes calculation, the support vector machine (SVM) is commonly used; however, SVM is time-consuming. In addition, the non-landslide grid cells are selected randomly and/or subjectively, which may result in unreasonable training and validating data for the machine learning models. This study proposes the self-organizing-map (SOM) network-based extreme learning machine (ELM) model to calculate the landslide susceptibility indexes. Wanzhou district in Three Gorges Reservoir Area is selected as the study area. Nine environmental factors are chosen as input variables and 639 investigated landslides are used as recorded landslides. First, an initial landslide susceptibility map is produced using the SOM network, and the reasonable non-landslide grid cells are subsequently selected from the very low susceptible area. Next, the final landslide susceptibility map is produced using the ELM model based on the recorded landslides and reasonable non-landslide grid cells. The single ELM model which selects the non-landslide grid cells randomly, and the SOM network-based SVM model are used for comparisons. It is concluded that the SOM-ELM model possesses higher success and prediction rates than the single ELM and SOM-SVM models, and the ELM has a considerably higher prediction efficiency than the SVM

ACS Style

Faming Huang; Kunlong Yin; Jinsong Huang; Lei Gui; Peng Wang. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Engineering Geology 2017, 223, 11 -22.

AMA Style

Faming Huang, Kunlong Yin, Jinsong Huang, Lei Gui, Peng Wang. Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine. Engineering Geology. 2017; 223 ():11-22.

Chicago/Turabian Style

Faming Huang; Kunlong Yin; Jinsong Huang; Lei Gui; Peng Wang. 2017. "Landslide susceptibility mapping based on self-organizing-map network and extreme learning machine." Engineering Geology 223, no. : 11-22.

Journal article
Published: 10 April 2017 in Journal of Hydroinformatics
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Many nonlinear models have been proposed to forecast groundwater level. However, the evidence of chaos in groundwater levels in landslide has not been explored. In addition, linear correlation analyses are used to determine the input and output variables for the nonlinear models. Linear correlation analyses are unable to capture the nonlinear relationships between the input and output variables. This paper proposes to use chaos theory to select the input and output variables for nonlinear models. The nonlinear model is constructed based on support vector machine (SVM). The parameters of SVM are obtained by particle swarm optimization (PSO). The proposed PSO-SVM model based on chaos theory (chaotic PSO-SVM) is applied to predict the daily groundwater levels in Huayuan landslide and the weekly, monthly groundwater levels in Baijiabao landslide in the Three Gorges Reservoir Area in China. The results show that there are chaos characteristics in the groundwater levels. The linear correlation analysis based PSO-SVM (linear PSO-SVM) and chaos theory-based back-propagation neural network (chaotic BPNN) are also applied for the purpose of comparison. The results show that the chaotic PSO-SVM model has higher prediction accuracy than the linear PSO-SVM and chaotic BPNN models for the test data considered.

ACS Style

Faming Huang; Jinsong Huang; Shui-Hua Jiang; Chuangbing Zhou. Prediction of groundwater levels using evidence of chaos and support vector machine. Journal of Hydroinformatics 2017, 19, 586 -606.

AMA Style

Faming Huang, Jinsong Huang, Shui-Hua Jiang, Chuangbing Zhou. Prediction of groundwater levels using evidence of chaos and support vector machine. Journal of Hydroinformatics. 2017; 19 (4):586-606.

Chicago/Turabian Style

Faming Huang; Jinsong Huang; Shui-Hua Jiang; Chuangbing Zhou. 2017. "Prediction of groundwater levels using evidence of chaos and support vector machine." Journal of Hydroinformatics 19, no. 4: 586-606.

Journal article
Published: 01 February 2017 in Engineering Geology
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ACS Style

Faming Huang; Jinsong Huang; Shuihua Jiang; Chuangbing Zhou. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Engineering Geology 2017, 218, 173 -186.

AMA Style

Faming Huang, Jinsong Huang, Shuihua Jiang, Chuangbing Zhou. Landslide displacement prediction based on multivariate chaotic model and extreme learning machine. Engineering Geology. 2017; 218 ():173-186.

Chicago/Turabian Style

Faming Huang; Jinsong Huang; Shuihua Jiang; Chuangbing Zhou. 2017. "Landslide displacement prediction based on multivariate chaotic model and extreme learning machine." Engineering Geology 218, no. : 173-186.