Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Tunnel squeezing is one of the major geological disasters that often occur during the construction of tunnels in weak rock masses subjected to high in situ stresses. It could cause shield jamming, budget overruns, and construction delays and could even lead to tunnel instability and casualties. Therefore, accurate prediction or identification of tunnel squeezing is extremely important in the design and construction of tunnels. This study presents a modified application of a multiclass support vector machine (SVM) to predict tunnel squeezing based on four parameters, that is, diameter (D), buried depth (H), support stiffness (K), and rock tunneling quality index (Q). We compiled a database from the literature, including 117 case histories obtained from different countries such as India, Nepal, and Bhutan, to train the multiclass SVM model. The proposed model was validated using 8-fold cross validation, and the average error percentage was approximately 11.87%. Compared with existing approaches, the proposed multiclass SVM model yields a better performance in predictive accuracy. More importantly, one could estimate the severity of potential squeezing problems based on the predicted squeezing categories/classes.

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Demand Forecasting Model of Port Critical Spare Parts Based on PSO-LSSVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.545 ◽

2013 ◽

Vol 433-435 ◽

pp. 545-549

Author(s):

Zhi Jie Song ◽

Zan Fu ◽

Han Wang ◽

Gui Bin Hou

Keyword(s):

Historical Data ◽

Demand Forecasting ◽

Spare Parts ◽

Influential Factors ◽

Support Vector ◽

Proposed Model ◽

Svm Model ◽

Vector Machines ◽

Demand Forecasting Model

Demand forecasting for port critical spare parts (CSP) is notoriously difficult as it is expensive, lumpy and intermittent with high variability. In this paper, some influential factors which have an effect on CSP consumption were proposed according to port CSP characteristics and historical data. Combined with the influential factors, a least squares support vector machines (LS-SVM) model optimized by particle swarm optimization (PSO) was developed to forecast the demand. And the effectiveness of the model is demonstrated through a real case study, which shows that the proposed model can forecast the demand of port CSP more accurately, and effectively reduce inventory backlog.

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Novel Genetic Variants of Hepatitis B Virus in Fulminant Hepatitis

Journal of Pathogens ◽

10.1155/2017/1231204 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Jack Bee Chook ◽

Yun Fong Ngeow ◽

Kok Keng Tee ◽

Suat Cheng Peh ◽

Rosmawati Mohamed

Keyword(s):

Hepatitis B Virus ◽

Hepatitis B ◽

Predictive Value ◽

Fulminant Hepatitis ◽

Predictive Accuracy ◽

Stop Codon ◽

Acute Infection ◽

Support Vector ◽

Svm Model ◽

B Virus

Fulminant hepatitis (FH) is a life-threatening liver disease characterised by intense immune attack and massive liver cell death. The common precore stop codon mutation of hepatitis B virus (HBV), A1896, is frequently associated with FH, but lacks specificity. This study attempts to uncover all possible viral nucleotides that are specifically associated with FH through a compiled sequence analysis of FH and non-FH cases from acute infection. We retrieved 67 FH and 280 acute non-FH cases of hepatitis B from GenBank and applied support vector machine (SVM) model to seek candidate nucleotides highly predictive of FH. Six best candidates with top predictive accuracy, 92.5%, were used to build a SVM model; they are C2129 (85.3%), T720 (83.0%), Y2131 (82.4%), T2013 (82.1%), K2048 (82.1%), and A2512 (82.1%). This model gave a high specificity (99.3%), positive predictive value (95.6%), and negative predictive value (92.1%), but only moderate sensitivity (64.2%). We successfully built a SVM model comprising six variants that are highly predictive and specific for FH: four in the core region and one each in the polymerase and the surface regions. These variants indicate that intracellular virion/core retention could play an important role in the progression to FH.

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Landslide Susceptibility Assessment Based on Different MaChine Learning Methods in Zhaoping County of Eastern Guangxi

Remote Sensing ◽

10.3390/rs13183573 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3573

Author(s):

Chunfang Kong ◽

Yiping Tian ◽

Xiaogang Ma ◽

Zhengping Weng ◽

Zhiting Zhang ◽

...

Keyword(s):

Particle Swarm Optimization ◽

Random Forest ◽

Landslide Susceptibility ◽

Roc Curves ◽

Support Vector ◽

Swarm Optimization ◽

Svm Model ◽

Vector Machines ◽

Susceptibility Evaluation ◽

Landslide Disaster

Regarding the ever increasing and frequent occurrence of serious landslide disaster in eastern Guangxi, the current study was implemented to adopt support vector machines (SVM), particle swarm optimization support vector machines (PSO-SVM), random forest (RF), and particle swarm optimization random forest (PSO-RF) methods to assess landslide susceptibility in Zhaoping County. To this end, 10 landslide disaster-related variables including digital elevation model (DEM)-derived, meteorology-derived, Landsat8-derived, geology-derived, and human activities factors were provided. Of 345 landslide disaster locations found, 70% were used to train the models, and the rest of them were performed for model verification. The aforementioned four models were run, and landslide susceptibility evaluation maps were produced. Then, receiver operating characteristics (ROC) curves, statistical analysis, and field investigation were performed to test and verify the efficiency of these models. Analysis and comparison of the results denoted that all four landslide models performed well for the landslide susceptibility evaluation as indicated by the area under curve (AUC) values of ROC curves from 0.863 to 0.934. Among them, it has been shown that the PSO-RF model has the highest accuracy in comparison to other landslide models, followed by the PSO-SVM model, the RF model, and the SVM model. Moreover, the results also showed that the PSO algorithm has a good effect on SVM and RF models. Furthermore, the landslide models devolved in the present study are promising methods that could be transferred to other regions for landslide susceptibility evaluation. In addition, the evaluation results can provide suggestions for disaster reduction and prevention in Zhaoping County of eastern Guangxi.

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Abutment scour in clear-water and live-bed conditions by GMDH network

Water Science & Technology ◽

10.2166/wst.2013.670 ◽

2013 ◽

Vol 67 (5) ◽

pp. 1121-1128 ◽

Cited By ~ 32

Author(s):

Mohammad Najafzadeh ◽

Gholam-Abbas Barani ◽

Masoud Reza Hessami Kermani

Keyword(s):

Back Propagation ◽

Support Vector ◽

Group Method ◽

Scour Depth ◽

Back Propagation Algorithm ◽

Clear Water ◽

Sediment Size ◽

Svm Model ◽

Vector Machines ◽

Abutment Scour

In the present study, the Group Method of Data Handling (GMDH) network has been utilized to predict abutments scour depth for both clear-water and live-bed conditions. The GMDH network was developed using a Back Propagation algorithm (BP). Input parameters that were considered as effective variables on abutment scour depth included properties of sediment size, geometry of bridge abutments, and properties of approaching flow. Training and testing performances of the GMDH network were carried out using dimensionless parameters that were collected from the literature. The testing results were compared with those obtained using the Support Vector Machines (SVM) model and the traditional equations. The GMDH network predicted the abutment scour depth with lower error (RMSE (root mean square error) = 0.29 and MAPE (mean absolute percentage of error) = 0.99) and higher (R = 0.98) accuracy than those performed using the SVM model and the traditional equations.

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Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽

10.3390/molecules25061442 ◽

2020 ◽

Vol 25 (6) ◽

pp. 1442 ◽

Cited By ~ 2

Author(s):

Tao Shen ◽

Hong Yu ◽

Yuan-Zhong Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Related Species ◽

Predictive Accuracy ◽

Classification Model ◽

Venn Diagram ◽

Support Vector ◽

Stacked Generalization ◽

Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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An Approach Using Adaptive Weighted Least Squares Support Vector Machines Coupled with Modified Ant Lion Optimizer for Dam Deformation Prediction

Mathematical Problems in Engineering ◽

10.1155/2020/9434065 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Yijun Chen ◽

Chongshi Gu ◽

Chenfei Shao ◽

Hao Gu ◽

Dongjian Zheng ◽

...

Keyword(s):

Support Vector Machines ◽

Least Squares ◽

Weighted Least Squares ◽

Support Vector ◽

Deformation Prediction ◽

Ant Lion Optimizer ◽

Proposed Model ◽

Vector Machines ◽

Ant Lion ◽

Dam Deformation

A dam deformation prediction model based on adaptive weighted least squares support vector machines (AWLSSVM) coupled with modified Ant Lion Optimization (ALO) is proposed, which can be utilized to evaluate the operational states of concrete dams. First, the Ant Lion Optimizer, a novel metaheuristic algorithm, is used to determine the punishment factor and kernel width in the least squares support vector machine (LSSVM) model, which simulates the hunting process of antlions in nature. Second, aiming to solve the premature convergence phenomenon, Levy flight is introduced into the ALO to improve the global optimization ability. Third, according to the statistical characteristics of the datum error, an improved normal distribution weighting rule is applied to update the weighted value of data samples based on the learning result of the LSSVM model. Moreover, taking a concrete arch dam in China as an example, the horizontal displacement recorded by a pendulum is used as a study object. The accuracy and validity of the proposed model are verified and evaluated based on the four evaluating criteria, and the results of the proposed model are compared with those of well-established models. The simulation results demonstrate that the proposed model outperforms other models and effectively overcomes the influence of outliers on the performance of the model. It also has high prediction accuracy, produces excellent generalization performance, and can be a promising alternative technique for the analysis and prediction of dam deformation and other fields, including flood interval prediction, the stock price market, and wind speed forecasting.

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The Application of Machine Learning to a General Risk–Need Assessment Instrument in the Prediction of Criminal Recidivism

Criminal Justice and Behavior ◽

10.1177/0093854820969753 ◽

2020 ◽

pp. 009385482096975

Author(s):

Mehdi Ghasemi ◽

Daniel Anvari ◽

Mahshid Atapour ◽

J. Stephen wormith ◽

Keira C. Stockdale ◽

...

Keyword(s):

Machine Learning ◽

Predictive Accuracy ◽

Characteristic Curve ◽

Assessment Instrument ◽

Support Vector ◽

Data Set ◽

Applied Machine Learning ◽

Vector Machines ◽

Individual Scores

The Level of Service/Case Management Inventory (LS/CMI) is one of the most frequently used tools to assess criminogenic risk–need in justice-involved individuals. Meta-analytic research demonstrates strong predictive accuracy for various recidivism outcomes. In this exploratory study, we applied machine learning (ML) algorithms (decision trees, random forests, and support vector machines) to a data set with nearly 100,000 LS/CMI administrations to provincial corrections clientele in Ontario, Canada, and approximately 3 years follow-up. The overall accuracies and areas under the receiver operating characteristic curve (AUCs) were comparable, although ML outperformed LS/CMI in terms of predictive accuracy for the middle scores where it is hardest to predict the recidivism outcome. Moreover, ML improved the AUCs for individual scores to near 0.60, from 0.50 for the LS/CMI, indicating that ML also improves the ability to rank individuals according to their probability of recidivating. Potential considerations, applications, and future directions are discussed.

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Pulse Waveform Classification Using Support Vector Machine with Gaussian Time Warp Edit Distance Kernel

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/947254 ◽

2014 ◽

Vol 2014 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Danbing Jia ◽

Dongyu Zhang ◽

Naimin Li

Keyword(s):

Quantitative Research ◽

Edit Distance ◽

Difficult Problem ◽

Average Error ◽

Support Vector ◽

Pulse Waveform ◽

Vector Machines ◽

Processing Techniques ◽

Gaussian Time

Advances in signal processing techniques have provided effective tools for quantitative research in traditional Chinese pulse diagnosis. However, because of the inevitable intraclass variations of pulse patterns, the automatic classification of pulse waveforms has remained a difficult problem. Utilizing the new elastic metric, that is, time wrap edit distance (TWED), this paper proposes to address the problem under the support vector machines (SVM) framework by using the Gaussian TWED kernel function. The proposed method, SVM with GTWED kernel (GTWED-SVM), is evaluated on a dataset including 2470 pulse waveforms of five distinct patterns. The experimental results show that the proposed method achieves a lower average error rate than current pulse waveform classification methods.

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A Forecast Model of the Number of Containers for Containership Voyage

Algorithms ◽

10.3390/a11120193 ◽

2018 ◽

Vol 11 (12) ◽

pp. 193

Author(s):

Yuchuang Wang ◽

Guoyou Shi ◽

Xiaotong Sun

Keyword(s):

Container Terminal ◽

Forecast Model ◽

Gray Relational Analysis ◽

Support Vector ◽

Model Parameters ◽

Container Ship ◽

Kernel Support Vector Machine ◽

Proposed Model ◽

Svm Model ◽

Pass Through

Container ships must pass through multiple ports of call during a voyage. Therefore, forecasting container volume information at the port of origin followed by sending such information to subsequent ports is crucial for container terminal management and container stowage personnel. Numerous factors influence container allocation to container ships for a voyage, and the degree of influence varies, engendering a complex nonlinearity. Therefore, this paper proposes a model based on gray relational analysis (GRA) and mixed kernel support vector machine (SVM) for predicting container allocation to a container ship for a voyage. First, in this model, the weights of influencing factors are determined through GRA. Then, the weighted factors serve as the input of the SVM model, and SVM model parameters are optimized through a genetic algorithm. Numerical simulations revealed that the proposed model could effectively predict the number of containers for container ship voyage and that it exhibited strong generalization ability and high accuracy. Accordingly, this model provides a new method for predicting container volume for a voyage.

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Application of the Artificial Neural Network and Support Vector Machines in Forest Fire Prediction in the Guangxi Autonomous Region, China

Discrete Dynamics in Nature and Society ◽

10.1155/2020/5612650 ◽

2020 ◽

Vol 2020 ◽

pp. 1-14

Author(s):

Yudong Li ◽

Zhongke Feng ◽

Shilin Chen ◽

Ziyu Zhao ◽

Fengge Wang

Keyword(s):

Neural Network ◽

Support Vector Machines ◽

Forest Fire ◽

Bp Neural Network ◽

Forest Fires ◽

Prediction Accuracy ◽

Support Vector ◽

Autonomous Region ◽

Svm Model ◽

Vector Machines

The study of forest fire prediction is of great environmental and scientific significance. China’s Guangxi Autonomous Region has a high incidence rate of forest fires. At present, there is little research on forest fires in this area. The application of the artificial neural network and support vector machines (SVM) in forest fire prediction in this area can provide data for forest fire prevention and control in Guangxi. In this paper, based on Guangxi’s 2010–2018 satellite monitoring hotspot data, meteorology, terrain, vegetation, infrastructure, and socioeconomic data, the researchers determined the main forest fire driving factors in Guangxi. They used feature selection and backpropagation neural networks and radial basis SVM to build forest fire prediction models. Finally, the researchers use the accuracy, precision, and area under the characteristic curve (ROC-AUC) and other indicators to evaluate the predictive performance of the two models. The results showed that the prediction accuracy of the BP neural network and SVM is 92.16% and 89.89%, respectively. As both results are over 85%, the requirements of prediction accuracy is met. These results can be used for forest fire prediction in the Guangxi Autonomous Region. Specifically, the accuracy of the BP neural network was 0.93, which was higher than that of the SVM model (0.89); the recall of the SVM model was 0.84, which was lower than the BANN model (0.92), and the AUC value of the SVM model was 0.95, which was lower than the BP neural network model. The obtained results confirm that the BP neural network model can provide more prediction accuracy than support vector machines and is therefore more suitable for forest fire prediction in Guangxi, China. This research provides the necessary theoretical basis and data support for application in the field of forestry of the Guangxi Autonomous Region, China.

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