ORCID Profile
0000-0002-4428-7133
Current Organisations
Nanchang University
,
Christian Medical College Vellore
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Publisher: Editorial Office of Earth Science
Date: 2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Frontiers Media SA
Date: 03-09-2021
DOI: 10.3389/FEART.2021.731058
Abstract: For the issue of collapse susceptibility prediction (CSP), minimal attention has been paid to explore the uncertainty characteristics of different machine learning models predicting collapse susceptibility. In this study, six kinds of typical machine learning methods, namely, logistic regression (LR), radial basis function neural network (RBF), multilayer perceptron (MLP), support vector machine (SVM), chi-square automatic interactive detection decision tree (CHAID), and random forest (RF) models, are constructed to do CSP. In this regard, An’yuan County in China, with a total of 108 collapses and 11 related environmental factors acquired through remote sensing and GIS technologies, is selected as a case study. The spatial dataset is first constructed, and then these machine learning models are used to implement CSP. Finally, the uncertainty characteristics of the CSP results are explored according to the accuracies, mean values, and standard deviations of the collapse susceptibility indexes (CSIs) and the Kendall synergy coefficient test. In addition, Huichang County, China, is used as another study case to avoid the uncertainty of different study areas. Results show that 1) overall, all six kinds of machine learning models reasonably and accurately predict the collapse susceptibility in An’yuan County 2) the RF model has the highest prediction accuracy, followed by the CHAID, SVM, MLP, RBF, and LR models and 3) the CSP results of these models are significantly different, with the mean value (0.2718) and average rank (2.72) of RF being smaller than those of the other five models, followed by the CHAID (0.3210 and 3.29), SVM (0.3268 and 3.48), MLP (0.3354 and 3.64), RBF (0.3449 and 3.81), and LR (0.3496 and 4.06), and with a Kendall synergy coefficient value of 0.062. Conclusively, it is necessary to adopt a series of different machine learning models to predict collapse susceptibility for cross-validation and comparison. Furthermore, the RF model has the highest prediction accuracy and the lowest uncertainty of the CSP results of the machine learning models.
Publisher: Springer Science and Business Media LLC
Date: 29-06-2020
Publisher: MDPI AG
Date: 22-06-2017
DOI: 10.3390/W9070450
Abstract: It is significant to study the variations in the stability coefficients of hydrodynamic pressure landslides with different permeability coefficients affected by reservoir water level fluctuations and rainstorms. The Sifangbei landslide in Three Gorges Reservoir area is used as case study. Its stability coefficients are simulated based on saturated-unsaturated seepage theory and finite element analysis. The operating conditions of stability coefficients calculation are reservoir water level variations between 175 m and 145 m, different rates of reservoir water level fluctuations, and a three-day continuous rainstorm. Results show that the stability coefficient of the hydrodynamic pressure landslide decreases with the drawdown of the reservoir water level, and a rapid drawdown rate leads to a small stability coefficient when the permeability coefficient ranges from 1.16 × 10−6 m/s to 4.64 × 10−5 m/s. Additionally, the landslide stability coefficient increases as the reservoir water level increases, and a rapid increase in the water level leads to a high stability coefficient when the permeability coefficient ranges from 1.16 × 10−6 m/s to 4.64 × 10−5 m/s. The landslide stability coefficient initially decreases and then increases as the reservoir water level declines when the permeability coefficient is greater than 4.64 × 10−5 m/s. Moreover, for structures with the same landslide, the landslide stability coefficient is most sensitive to the change in the rate of reservoir water level drawdown when the permeability coefficient increases from 1.16 × 10−6 m/s to 1.16 × 10−4 m/s. Additionally, the rate of decrease in the stability coefficient increases as the permeability coefficient increases. Finally, the three-day rainstorm leads to a significant reduction in landslide stability, and the rate of decrease in the stability coefficient initially increases and then decreases as the permeability coefficient increases.
Publisher: Editorial Office of Earth Science
Date: 2019
Publisher: Informa UK Limited
Date: 2018
Publisher: IOP Publishing
Date: 10-2020
DOI: 10.1088/1755-1315/570/4/042049
Abstract: The landslide susceptibility prediction (LSP) is generally implemented using certain types of single models however, certain drawbacks exist in the single models e.g., it is difficult to clearly reflect the weights of landslide-related environmental factors if only the information value (IV) model is adopted. To overcome these limitations, this study proposes an IV-logistic regression (IV-LR) model for LSP. The landslides that occurred in the southern part of Chongyi County, China, are used as study cases. Nine environmental factors—elevation, slope, plane curvature, profile curvature, relief litude, distance to river, lithology, normalized difference vegetable index, and normalized difference built-up index—are adopted based on remote sensing and geographic information system. Certain landslide grid units and the same number of non-landslide grid units are used as the output variables of these models. The IV, LR, and IV-LR models are used to implement the LSP in the southern part of Chongyi County. The predicted landslides susceptibility in Chongyi County mostly occurred in areas with low elevations, close distance to rivers, carbonate lithology, low vegetation coverage rate, and densely populated areas. The results show that the prediction rate of the IV-LR model (80.4%) is higher than that of the LR model (76.8%), followed by the IV model (72.8%) they further demonstrate that the IV-LR model has its unique superiority and rationality compared with the IV and LR models.
Publisher: Elsevier BV
Date: 07-2021
Publisher: Elsevier BV
Date: 06-2017
Publisher: Elsevier BV
Date: 11-2021
Publisher: Springer Science and Business Media LLC
Date: 20-06-2019
DOI: 10.1038/S41598-019-45403-9
Abstract: Establishing an efficient regional landslide rainfall warning system plays an important role in landslide prevention. To forecast the performance of landslides with creep deformation at a regional scale, a black box model based on statistical analysis was proposed and was applied to Yunyang County in the Three Gorges Reservoir area (TGRA), China. The data s les were selected according to the characteristics of the landslide displacement monitoring data. Then, the rainfall criteria applied to different time periods were determined by correlation analysis between rainfall events and landslides and by numerical simulation on landslide movement under certain rainfall conditions. The cumulative rainfall thresholds that were determined relied on the displacement ratio model, which considered landslide scale characteristics and the statistical relationship between daily rainfall data and monthly displacement data. These thresholds were then applied to a warning system to determine a five-level warning partition of landslides with creep deformation in Yunyang County. Finally, landslide cases and displacement monitoring data were used to validate the accuracy of the model. The validation procedure showed that the warning results of the model fit well with actual conditions and that this model could provide the basis for early warning of landslides with creep deformation.
Publisher: MDPI AG
Date: 09-10-2017
DOI: 10.3390/W9100758
Publisher: Springer Science and Business Media LLC
Date: 27-02-2018
Publisher: MDPI AG
Date: 17-12-2020
DOI: 10.3390/RS12244134
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 2022
Publisher: Editorial Office of Earth Science
Date: 2020
Publisher: Editorial Office of Earth Science
Date: 2015
Publisher: Elsevier BV
Date: 11-2018
Publisher: MDPI AG
Date: 20-03-2021
DOI: 10.3390/IJGI10030184
Abstract: 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.
Publisher: IOP Publishing
Date: 07-2019
DOI: 10.1088/1755-1315/300/2/022025
Abstract: In order to accurately quantify the terrain complexity, a simple and accurate terrain complexity assessment (TCA) model is proposed. Taking Ningdu county in Jiangxi Province of China as an ex le, firstly, six terrain factors (named slope, topographic relief degree, surface cutting depth, surface roughness, elevation variation coefficient and topographic factors) of Ningdu county are extracted based on the Digital Elevation Model (DEM) with 30 m resolution and ARCGIS 10.2 software. Secondly, terrain complexity indexes of Ningdu County are obtained using k-means clustering. Results show that a current and effective spatial distribution characteristic of topographic complexity in Ningdu county is produced, and the very low and low terrain complexity indexes account for 33.28%, 28.35% respectively. The terrain complexity can be evaluated effectively by k-means clustering model. The terrain complexity can be provided for environmental protection and land use planning.
Publisher: MDPI AG
Date: 08-09-2020
DOI: 10.3390/IJGI9090539
Abstract: 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 ex le, 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 ex le, 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.
Publisher: Springer Science and Business Media LLC
Date: 11-09-2019
Publisher: Informa UK Limited
Date: 2021
Publisher: Editorial Office of Earth Science
Date: 2018
Publisher: Elsevier BV
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 07-07-2020
Publisher: Springer Science and Business Media LLC
Date: 15-04-2019
Publisher: IOP Publishing
Date: 07-2019
DOI: 10.1088/1755-1315/300/3/032010
Abstract: Poyang Lake is the largest freshwater lake in China, with a wide area and abundant species resources. It is a serious issue to protect and monitor the water quality of Poyang lake. This paper proposes to use principal component analysis (PCA) to evaluate the water pollution index of Poyang Lake. The input variables of PCA are the weekly monitoring water pollution factors including dissolved oxygen (DO), chemical oxygen demand (CODMn) and NH4+-N. The water quality monitoring station is in Hukou County of China from 2004 to 2014. Finally, a series of new water pollution indexes are generated by PCA to reflect the change characteristics of lake water pollution. The results can provide support for the comprehensive evaluation of lake water quality. Meanwhile, the results also discuss the variation in water pollution, which is practical and innovative.
Publisher: Elsevier BV
Date: 02-2017
Publisher: Wiley
Date: 16-08-2023
DOI: 10.1111/IMM.13655
Abstract: Chronic rhinosinusitis (CRS) represents chronic inflammation of the sinus mucosa characterised by dysfunction of the sinuses' natural defence mechanisms and induction of different inflammatory pathways ranging from a Th1 to a Th2 predominant polarisation. Recalcitrant CRS is associated with Staphylococcus aureus dominant mucosal biofilms however, S. aureus colonisation of the sinonasal mucosa has also been observed in healthy in iduals challenging the significance of S. aureus in CRS pathogenesis. We aimed to investigate the relationship between CRS key inflammatory markers, S. aureus biofilm properties/virulence genes and the severity of the disease. Tissue s les were collected during endoscopic sinus surgery from the ethmoid sinuses of CRS patients with (CRSwNP) and without (CRSsNP) nasal polyps and controls ( n = 59). CD3+ T‐cell subset frequencies and key inflammatory markers of CD4+ helper T cells were determined using FACS analysis. Sinonasal S. aureus clinical isolates were isolated ( n = 26), sequenced and grown into biofilm in vitro, followed by determining their properties, including metabolic activity, biomass, colony‐forming units and exoprotein production. Disease severity was assessed using Lund–Mackay radiologic scores, Lund–Kennedy endoscopic scores and SNOT22 quality of life scores. Our results showed that S. aureus biofilm properties and CRS severity scores correlated positively with total CD4+ T‐cell frequencies but looking into CD4+ T‐cell subsets showed an inverse correlation with Th1 and Th17 cell frequencies. CD4+ T‐cell frequencies were higher in patients harbouring lukF.PV‐ positive S. aureus while its regulatory and Th17 cell subset frequencies were lower in patients carrying sea− and sarT/U‐ positive S. aureus. Recalcitrant CRS is characterised by increased S. aureus biofilm properties in relation to increased total CD4+ helper T‐cell frequencies and reduced frequencies of its Th1, Th17 and regulatory T‐cell subsets. These findings offer insights into the pathophysiology of CRS and could lead to the development of more targeted therapies.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Wiley
Date: 07-06-2020
DOI: 10.1002/ALR.22566
Publisher: MDPI AG
Date: 04-02-2020
DOI: 10.3390/RS12030502
Abstract: 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.
Publisher: Mechanical Engineering Faculty in Slavonski Brod
Date: 04-2016
Publisher: Springer Science and Business Media LLC
Date: 03-09-2021
Publisher: Frontiers Media SA
Date: 18-11-2021
DOI: 10.3389/FEART.2021.783148
Abstract: Comparative pullout tests were carried out on model plate anchors in uncemented aeolian sand (UAS) and cement-stabilised aeolian sand (CAS) with different embedment ratios of the embedment depth ( H ) to the width ( D ) of the plate to examine the effectiveness of the insertion of cement in aeolian sand to enhance the uplift performance of plate anchors. Experimental results demonstrated that significant increases in failure resistance and uplift stiffness can be achieved, irrespective of embedment ratios of H / D , when a relatively small amount of cement (an optimal cement content of 6% by weight of dry aeolian sand determined by direct shear test in this study) was added to the aeolian sand backfill. However, distinct load–displacement responses were observed in all the tests on the model plate anchors embedded in CAS and UAS backfills: two-phase of pre-peak and post-peak behaviour in CAS and three-phase of initial linear, nonlinear transition to peak uplift resistance, and post-peak behaviour in UAS failure of the former started at tiny displacements and that of the latter appeared at large displacements. Therefore, the significant increases in uplift failure resistance and pre-peak uplift stiffness were limited to relatively low uplift displacements because of the brittle nature of the improved CAS backfills shear strength characteristics.
Publisher: MDPI AG
Date: 04-09-2019
DOI: 10.3390/APP9183664
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 07-12-2017
DOI: 10.1038/S41598-017-17507-7
Abstract: It is important to monitor the displacement time series and to explore the failure mechanism of reservoir landslide for early warning. Traditionally, it is a challenge to monitor the landslide displacements real-timely and automatically. Globe Position System (GPS) is considered as the best real-time monitoring technology, however, the accuracies of the landslide displacements monitored by GPS are not assessed effectively. A web-based GPS system is developed to monitor the landslide displacements real-timely and automatically in this study. And the discrete wavelet transform (DWT) is proposed to assess the accuracy of the GPS monitoring displacements. Wangmiao landslide in Three Gorges Reservoir area in China is used as case study. The results show that the web-based GPS system has advantages of high precision, real-time, remote control and automation for landslide monitoring the Root Mean Square Errors of the monitoring landslide displacements are less than 5 mm. Meanwhile, the results also show that a rapidly falling reservoir water level can trigger the reactivation of Wangmiao landslide. Heavy rainfall is also an important factor, but not a crucial component.
Publisher: MDPI AG
Date: 08-06-2020
DOI: 10.3390/IJGI9060377
Abstract: 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.
Publisher: IWA Publishing
Date: 10-04-2017
Abstract: 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.
Publisher: Elsevier BV
Date: 07-2022
Publisher: Springer Science and Business Media LLC
Date: 15-05-2020
Publisher: Editorial Office of Earth Science
Date: 2019
Publisher: Editorial Office of Earth Science
Date: 2021
Publisher: Springer Science and Business Media LLC
Date: 10-2016
Publisher: Informa UK Limited
Date: 2019
Publisher: Elsevier BV
Date: 03-2022
Publisher: MDPI AG
Date: 12-03-2020
DOI: 10.3390/S20061576
Abstract: 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.
Publisher: Elsevier BV
Date: 12-2019
Start Date: 2019
End Date: 2021
Funder: National Natural Science Foundation of China
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