scholarly journals Landslide susceptibility evaluation based on optimized support vector machine

2021 ◽  
Vol 4 ◽  
pp. 1-4
Author(s):  
Jiping Liu ◽  
Rongfu Lin ◽  
Shenghua Xu ◽  
Yong Wang ◽  
Xianghong Che ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Firefly Algorithm ◽  
Vegetation Index ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Susceptibility Map ◽  
Data Set ◽  
Actual Distribution

Abstract. Landslide is a natural disaster that has caused great property losses and human casualties in the world. To strengthen the target prevention and management level, ZhaShui county, Shaanxi province, is selected as the research area to evaluate the landslide susceptibility. First of all, under the premise of considering the correlation, 10 evaluation factors closely related to landslide disaster (i.e., elevation, rainfall, rock group, slope, slope aspect, vegetation index, landform, distance to residential area, distance to road, distance to river system) are taken together with non-landslide points, which are selected under multi-constraint conditions to form a sample data-set. Secondly, the sample dataset is substituted into the Support Vector Machine (SVM) model optimized by firefly algorithm for training and prediction. Finally, the result map was partitioned according to the natural discontinuous point method, and the landslide susceptibility map was obtained. The results show that the model optimized by the firefly algorithm has higher accuracy, and the landslide susceptibility results are more consistent with the actual distribution of disaster points.

scholarly journals Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling

Entropy ◽  
2018 ◽  
Vol 20 (11) ◽  
pp. 884 ◽  
Author(s):  
Tingyu Zhang ◽  
Ling Han ◽  
Wei Chen ◽  
Himan Shahabi
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Shaanxi Province ◽  
Slope Aspect ◽  
Support Vector ◽  
Operating Characteristics ◽  
Study Region ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The main purpose of the present study is to apply three classification models, namely, the index of entropy (IOE) model, the logistic regression (LR) model, and the support vector machine (SVM) model by radial basis function (RBF), to produce landslide susceptibility maps for the Fugu County of Shaanxi Province, China. Firstly, landslide locations were extracted from field investigation and aerial photographs, and a total of 194 landslide polygons were transformed into points to produce a landslide inventory map. Secondly, the landslide points were randomly split into two groups (70/30) for training and validation purposes, respectively. Then, 10 landslide explanatory variables, such as slope aspect, slope angle, altitude, lithology, mean annual precipitation, distance to roads, distance to rivers, distance to faults, land use, and normalized difference vegetation index (NDVI), were selected and the potential multicollinearity problems between these factors were detected by the Pearson Correlation Coefficient (PCC), the variance inflation factor (VIF), and tolerance (TOL). Subsequently, the landslide susceptibility maps for the study region were obtained using the IOE model, the LR–IOE, and the SVM–IOE model. Finally, the performance of these three models was verified and compared using the receiver operating characteristics (ROC) curve. The success rate results showed that the LR–IOE model has the highest accuracy (90.11%), followed by the IOE model (87.43%) and the SVM–IOE model (86.53%). Similarly, the AUC values also showed that the prediction accuracy expresses a similar result, with the LR–IOE model having the highest accuracy (81.84%), followed by the IOE model (76.86%) and the SVM–IOE model (76.61%). Thus, the landslide susceptibility map (LSM) for the study region can provide an effective reference for the Fugu County government to properly address land planning and mitigate landslide risk.

scholarly journals Landslide Susceptibility Mapping Using Different GIS-Based Bivariate Models

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1402 ◽  
Author(s):  
Nohani ◽  
Moharrami ◽  
Sharafi ◽  
Khosravi ◽  
Pradhan ◽  
...  
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Aerial Photographs ◽  
Landslide Susceptibility Mapping ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Conditioning Factors ◽  
Bivariate Models

Landslides are the most frequent phenomenon in the northern part of Iran, which cause considerable financial and life damages every year. One of the most widely used approaches to reduce these damages is preparing a landslide susceptibility map (LSM) using suitable methods and selecting the proper conditioning factors. The current study is aimed at comparing four bivariate models, namely the frequency ratio (FR), Shannon entropy (SE), weights of evidence (WoE), and evidential belief function (EBF), for a LSM of Klijanrestagh Watershed, Iran. Firstly, 109 locations of landslides were obtained from field surveys and interpretation of aerial photographs. Then, the locations were categorized into two groups of 70% (74 locations) and 30% (35 locations), randomly, for modeling and validation processes, respectively. Then, 10 conditioning factors of slope aspect, curvature, elevation, distance from fault, lithology, normalized difference vegetation index (NDVI), distance from the river, distance from the road, the slope angle, and land use were determined to construct the spatial database. From the results of multicollinearity, it was concluded that no collinearity existed between the 10 considered conditioning factors in the occurrence of landslides. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) were used for validation of the four achieved LSMs. The AUC results introduced the success rates of 0.8, 0.86, 0.84, and 0.85 for EBF, WoE, SE, and FR, respectively. Also, they indicated that the rates of prediction were 0.84, 0.83, 0.82, and 0.79 for WoE, FR, SE, and EBF, respectively. Therefore, the WoE model, having the highest AUC, was the most accurate method among the four implemented methods in identifying the regions at risk of future landslides in the study area. The outcomes of this research are useful and essential for the government, planners, decision makers, researchers, and general land-use planners in the study area.

scholarly journals Integration of spatial and temporal data for the definition of different landslide hazard scenarios in the area north of Lisbon (Portugal)

2004 ◽  
Vol 4 (1) ◽  
pp. 133-146 ◽  
Author(s):  
J. L. Zêzere ◽  
E. Reis ◽  
R. Garcia ◽  
S. Oliveira ◽  
M. L. Rodrigues ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Landslide Hazard ◽  
Slope Instability ◽  
Rate Curve ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Return Periods ◽  
Data Set ◽  
Prediction Rate ◽  
Definition Of

Abstract. A general methodology for the probabilistic evaluation of landslide hazard is applied, taking in account both the landslide susceptibility and the instability triggering factors, mainly rainfall. The method is applied in the Fanhões-Trancão test site (north of Lisbon, Portugal) where 100 shallow translational slides were mapped and integrated into a GIS database. For the landslide susceptibility assessment it is assumed that future landslides can be predicted by statistical relationships between past landslides and the spatial data set of the predisposing factors (slope angle, slope aspect, transversal slope profile, lithology, superficial deposits, geomorphology, and land use). Susceptibility is evaluated using algorithms based on statistical/probabilistic analysis (Bayesian model) over unique-condition terrain units in a raster basis. The landslide susceptibility map is prepared by sorting all pixels according to the pixel susceptibility value in descending order. In order to validate the results of the susceptibility ana- lysis, the landslide data set is divided in two parts, using a temporal criterion. The first subset is used for obtaining a prediction image and the second subset is compared with the prediction results for validation. The obtained prediction-rate curve is used for the quantitative interpretation of the initial susceptibility map. Landslides in the study area are triggered by rainfall. The integration of triggering information in hazard assessment includes (i) the definition of thresholds of rainfall (quantity-duration) responsible for past landslide events; (ii) the calculation of the relevant return periods; (iii) the assumption that the same rainfall patterns (quantity/duration) which produced slope instability in the past will produce the same effects in the future (i.e. same types of landslides and same total affected area). The landslide hazard is present as the probability of each pixel to be affected by a slope movement, and results from the coupling between the susceptibility map, the prediction-rate curve, and the return periods of critical rainfall events, on a scenario basis. Using this methodology, different hazard scenarios were assessed, corresponding to different rain paths with different return periods.

scholarly journals A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shuai Zhao ◽  
Zhou Zhao
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Learning Algorithm ◽  
Slope Angle ◽  
Susceptibility Mapping ◽  
Landslide Susceptibility Mapping ◽  
Training Dataset ◽  
Validation Dataset ◽  
Slope Aspect ◽  
Support Vector

The main purpose of this study aims to apply and compare the rationality of landslide susceptibility maps using support vector machine (SVM) and particle swarm optimization coupled with support vector machine (PSO-SVM) models in Lueyang County, China, enhance the connection with the natural terrain, and analyze the application of grid units and slope units. A total of 186 landslide locations were identified by earlier reports and field surveys. The landslide inventory was randomly divided into two parts: 70% for training dataset and 30% for validation dataset. Based on the multisource data and geological environment, 16 landslide conditioning factors were selected, including control factors and triggering factors (i.e., altitude, slope angle, slope aspect, plan curvature, profile curvature, SPI, TPI, TRI, lithology, distance to faults, TWI, distance to rivers, NDVI, distance to roads, land use, and rainfall). The susceptibility between each conditioning factor and landslide was deduced using a certainty factor model. Subsequently, combined with grid units and slope units, the landslide susceptibility models were carried out by using SVM and PSO-SVM methods. The precision capability of the landslide susceptibility mapping produced by different models and units was verified through a receiver operating characteristic (ROC) curve. The results showed that the PSO-SVM model based on slope units had the best performance in landslide susceptibility mapping, and the area under the curve (AUC) values of training and validation datasets are 0.945 and 0.9245, respectively. Hence, the machine learning algorithm coupled with slope units can be considered a reliable and effective technique in landslide susceptibility mapping.

scholarly journals Performance Evaluation and Comparison of Bivariate Statistical-Based Artificial Intelligence Algorithms for Spatial Prediction of Landslides

2020 ◽  
Vol 9 (12) ◽  
pp. 696
Author(s):  
Wei Chen ◽  
Zenghui Sun ◽  
Xia Zhao ◽  
Xinxiang Lei ◽  
Ataollah Shirzadi ◽  
...  
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Slope Angle ◽  
Spatial Prediction ◽  
Slope Aspect ◽  
Support Vector ◽  
Topographic Wetness Index ◽  
Bivariate Model ◽  
Susceptibility Maps ◽  
Landslide Susceptibility Maps

The purpose of this study is to compare nine models, composed of certainty factors (CFs), weights of evidence (WoE), evidential belief function (EBF) and two machine learning models, namely random forest (RF) and support vector machine (SVM). In the first step, fifteen landslide conditioning factors were selected to prepare thematic maps, including slope aspect, slope angle, elevation, stream power index (SPI), sediment transport index (STI), topographic wetness index (TWI), plan curvature, profile curvature, land use, normalized difference vegetation index (NDVI), soil, lithology, rainfall, distance to rivers and distance to roads. In the second step, 152 landslides were randomly divided into two groups at a ratio of 70/30 as the training and validation datasets. In the third step, the weights of the CF, WoE and EBF models for conditioning factor were calculated separately, and the weights were used to generate the landslide susceptibility maps. The weights of each bivariate model were substituted into the RF and SVM models, respectively, and six integrated models and landslide susceptibility maps were obtained. In the fourth step, the receiver operating characteristic (ROC) curve and related parameters were used for verification and comparison, and then the success rate curve and the prediction rate curves were used for re-analysis. The comprehensive results showed that the hybrid model is superior to the bivariate model, and all nine models have excellent performance. The WoE–RF model has the highest predictive ability (AUC_T: 0.9993, AUC_P: 0.8968). The landslide susceptibility maps produced in this study can be used to manage landslide hazard and risk in Linyou County and other similar areas.

scholarly journals New Ensemble Models for Shallow Landslide Susceptibility Modeling in a Semi-Arid Watershed

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 743 ◽  
Author(s):  
Dieu Tien Bui ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Marten Geertsema ◽  
Ebrahim Omidvar ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Landslide Susceptibility ◽  
Goodness Of Fit ◽  
Model Comparison ◽  
Characteristic Curve ◽  
Support Vector ◽  
Susceptibility Map ◽  
Random Subspace ◽  
Ensemble Models ◽  
Conditioning Factors

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of support vector machine (SVM) was employed as a base classifier, and four Meta/ensemble classifiers, including Adaboost (AB), bagging (BA), rotation forest (RF), and random subspace (RS), were used to construct new ensemble models. SVM has been used previously to spatially predict landslides, but not together with its ensembles. We selected 20 conditioning factors and randomly portioned 98 landslide locations into training (70%) and validating (30%) groups. Several statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC), were used for model comparison and validation. Using the One-R Attribute Evaluation (ORAE) technique, we found that all 20 conditioning factors were significant in identifying landslide locations, but “distance to road” was found to be the most important. The RS (AUC = 0.837) and RF (AUC = 0.834) significantly improved the goodness-of-fit and prediction accuracy of the SVM (AUC = 0.810), whereas the BA (AUC = 0.807) and AB (AUC = 0.779) did not. The random subspace based support vector machine (RSSVM) model is a promising technique for helping to better manage land in landslide-prone areas.

scholarly journals Landslide Susceptibility Assessment Using Integrated Deep Learning Algorithm along the China-Nepal Highway

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4436 ◽  
Author(s):  
Liming Xiao ◽  
Yonghong Zhang ◽  
Gongzhuang Peng
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Learning Algorithm ◽  
Dynamic Change ◽  
Slope Angle ◽  
Machine Learning Algorithms ◽  
Sensor Data ◽  
Slope Aspect ◽  
Support Vector ◽  
Important Indicator

The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.

scholarly journals Landslide susceptibility analysis by means of event-based multi-temporal landslide inventories

2015 ◽  
Vol 3 (2) ◽  
pp. 1137-1173 ◽  
Author(s):  
C. M. Tseng ◽  
C. W. Lin ◽  
W. D. Hsieh
Keyword(s):  
Landslide Susceptibility ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landslide Inventory ◽  
Weight Of Evidence ◽  
Slope Aspect ◽  
Susceptibility Map ◽  
Study Region ◽  
Landslide Occurrence ◽  
Susceptibility Analysis

Abstract. This study uses landslide inventory of a single typhoon event and Weight of Evidence (WOE) analysis to establish landslide susceptibility map of the Laonung River in southern Taiwan. Eight factors including lithology, elevation, slope, slope aspect, landform, Normalized Difference Vegetation Index (NDVI), distance to geological structure, and distance to stream are used to evaluate the susceptibility of landslide. Effect analysis and the assessment of grouped factors showed that lithology, slope, elevation, and NDVI are the dominant factors of landslides in the study area. Landslide susceptibility analysis with these four factors achieves over 90% of the AUC (area under curve) of the success rate curve of all eight factors. Four landslide susceptibility models for four typhoons from 2007 to 2009 are established, and each model is cross validated. Results indicate that the best model should be constructed by using landslide inventory close to the landslide occurrence threshold and should reflect the most common spatial rainfall pattern in the study region for ideal simulation and validation results. The prediction accuracy of the best model in this study reached 90.2%. The two highest susceptibility categories (very high and high levels) cover around 80% of the total landslides in the study area.

A Computational Method for the Identification of Endolysins and Autolysins

2020 ◽  
Vol 27 (4) ◽  
pp. 329-336 ◽  
Author(s):  
Lei Xu ◽  
Guangmin Liang ◽  
Baowen Chen ◽  
Xu Tan ◽  
Huaikun Xiang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cell Wall ◽  
Experimental Results ◽  
Computational Method ◽  
Lytic Enzyme ◽  
Support Vector ◽  
Lytic Enzymes ◽  
Data Set ◽  
Optimal Feature ◽  
Better Than

Background: Cell lytic enzyme is a kind of highly evolved protein, which can destroy the cell structure and kill the bacteria. Compared with antibiotics, cell lytic enzyme will not cause serious problem of drug resistance of pathogenic bacteria. Thus, the study of cell wall lytic enzymes aims at finding an efficient way for curing bacteria infectious. Compared with using antibiotics, the problem of drug resistance becomes more serious. Therefore, it is a good choice for curing bacterial infections by using cell lytic enzymes. Cell lytic enzyme includes endolysin and autolysin and the difference between them is the purpose of the break of cell wall. The identification of the type of cell lytic enzymes is meaningful for the study of cell wall enzymes. Objective: In this article, our motivation is to predict the type of cell lytic enzyme. Cell lytic enzyme is helpful for killing bacteria, so it is meaningful for study the type of cell lytic enzyme. However, it is time consuming to detect the type of cell lytic enzyme by experimental methods. Thus, an efficient computational method for the type of cell lytic enzyme prediction is proposed in our work. Method: We propose a computational method for the prediction of endolysin and autolysin. First, a data set containing 27 endolysins and 41 autolysins is built. Then the protein is represented by tripeptides composition. The features are selected with larger confidence degree. At last, the classifier is trained by the labeled vectors based on support vector machine. The learned classifier is used to predict the type of cell lytic enzyme. Results: Following the proposed method, the experimental results show that the overall accuracy can attain 97.06%, when 44 features are selected. Compared with Ding's method, our method improves the overall accuracy by nearly 4.5% ((97.06-92.9)/92.9%). The performance of our proposed method is stable, when the selected feature number is from 40 to 70. The overall accuracy of tripeptides optimal feature set is 94.12%, and the overall accuracy of Chou's amphiphilic PseAAC method is 76.2%. The experimental results also demonstrate that the overall accuracy is improved by nearly 18% when using the tripeptides optimal feature set. Conclusion: The paper proposed an efficient method for identifying endolysin and autolysin. In this paper, support vector machine is used to predict the type of cell lytic enzyme. The experimental results show that the overall accuracy of the proposed method is 94.12%, which is better than some existing methods. In conclusion, the selected 44 features can improve the overall accuracy for identification of the type of cell lytic enzyme. Support vector machine performs better than other classifiers when using the selected feature set on the benchmark data set.

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