scholarly journals A Global Optimization-Based Method for the Prediction of Water Inrush Hazard from Mining Floor

Water ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 1618 ◽  
Author(s):  
Dan Ma ◽  
Hongyu Duan ◽  
Xin Cai ◽  
Zhenhua Li ◽  
Qiang Li ◽  
...  
Keyword(s):  
Global Optimization ◽  
Water Pressure ◽  
Water Inrush ◽  
Hybrid Genetic Algorithm ◽  
Function Parameter ◽  
Support Vector ◽  
Suitable Model ◽  
Svm Model ◽  
Highly Nonlinear ◽  
Mine Floor

Water inrush hazards can be effectively reduced by a reasonable and accurate soft-measuring method on the water inrush quantity from the mine floor. This is quite important for safe mining. However, there is a highly nonlinear relationship between the water outburst from coal seam floors and geological structure, hydrogeology, aquifer, water pressure, water-resisting strata, mining damage, fault and other factors. Therefore, it is difficult to establish a suitable model by traditional methods to forecast the water inrush quantity from the mine floor. Modeling methods developed in other fields can provide adequate models for rock behavior on water inrush. In this study, a new forecast system, which is based on a hybrid genetic algorithm (GA) with the support vector machine (SVM) algorithm, a model structure and the related parameters are proposed simultaneously on water inrush prediction. With the advantages of powerful global optimization functions, implicit parallelism and high stability of the GA, the penalty coefficient, insensitivity coefficient and kernel function parameter of the SVM model are determined as approximately optimal automatically in the spatial dimension. All of these characteristics greatly improve the accuracy and usable range of the SVM model. Testing results show that GA has a useful ability in finding optimal parameters of a SVM model. The performance of the GA optimized SVM (GA-SVM) is superior to the SVM model. The GA-SVM enables the prediction of water inrush and provides a promising solution to the predictive problem for relevant industries.

scholarly journals Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Lijun Wang ◽  
Shengfei Ji ◽  
Nanyang Ji
Keyword(s):  
Support Vector Machine ◽  
Global Optimization ◽  
Fault Diagnosis ◽  
Model Simulation ◽  
Rolling Bearing ◽  
Support Vector ◽  
Denoising Method ◽  
Svm Algorithm ◽  
Svm Model ◽  
Shuffled Frog Leaping

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.

scholarly journals Predicting Freeway Travel Time Using Multiple- Source Heterogeneous Data Integration

2018 ◽  
Vol 9 (1) ◽  
pp. 104 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu ◽  
Lee Han
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Adverse Weather

Freeway travel time is influenced by many factors including traffic volume, adverse weather, accidents, traffic control, and so on. We employ the multiple source data-mining method to analyze freeway travel time. We collected toll data, weather data, traffic accident disposal logs, and other historical data from Freeway G5513 in Hunan Province, China. Using the Support Vector Machine (SVM), we proposed the travel time predicting model founded on these databases. The new SVM model can simulate the nonlinear relationship between travel time and those factors. In order to improve the precision of the SVM model, we applied the Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with the Back Propagation (BP) neural network and a common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model, respectively.

scholarly journals Data-driven runoff forecasting for Minjiang River: a case study

2020 ◽  
Vol 20 (6) ◽  
pp. 2284-2295
Author(s):  
Yuqiang Wu ◽  
Qinhui Wang ◽  
Ge Li ◽  
Jidong Li
Keyword(s):  
Optimal Allocation ◽  
Data Driven ◽  
Function Parameter ◽  
Support Vector ◽  
Runoff Forecasting ◽  
Minjiang River ◽  
Svm Model ◽  
Runoff Prediction

Abstract Long-term runoff forecasting has the characteristics of a long forecast period, which can be widely applied in environmental protection, hydropower operation, flood prevention and waterlogging management, water transport management, and optimal allocation of water resources. Many models and methods are currently used for runoff prediction, and data-driven models for runoff prediction are now mainstream methods, but their prediction accuracy cannot meet the needs of production departments. To this end, the present research starts with this method and, based on a support vector machine (SVM), it introduces ant colony optimization (ACO) to optimize its penalty coefficient C, Kernel function parameter g, and insensitivity coefficient p, to construct a data-driven ACO-SVM model. The validity of the method is confirmed by taking the Minjiang River Basin as an example. The results show that the runoff predicted by use of ACO-SVM is more accurate than that of the default parameter SVM and the Bayesian method.

scholarly journals Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

2021 ◽  
Vol 10 (11) ◽  
pp. 766
Author(s):  
Xishihui Du ◽  
Kefa Zhou ◽  
Yao Cui ◽  
Jinlin Wang ◽  
Shuguang Zhou
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Hybrid Genetic Algorithm ◽  
Geochemical Data ◽  
Support Vector ◽  
Adaptive Optimization ◽  
Spatial Efficiency ◽  
Svm Model ◽  
Mineral Prospectivity ◽  
Optimization Search

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

scholarly journals Predicting Freeway Travelling Time Using Multiple-Source Data

2018 ◽  
Author(s):  
Kejun Long ◽  
Wukai Yao ◽  
Jian Gu ◽  
Wei Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Multiple Source ◽  
Function Parameter ◽  
Support Vector ◽  
Model Parameters ◽  
Svm Model ◽  
Source Data ◽  
Travelling Time

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

scholarly journals Random forest age estimation model based on length of left hand bone for Asian population

2020 ◽  
Vol 10 (1) ◽  
pp. 549
Author(s):  
Mohd Faaizie Darmawan ◽  
Ahmad Firdaus Zainal Abidin ◽  
Shahreen Kasim ◽  
Tole Sutikno ◽  
Rahmat Budiarto
Keyword(s):  
Random Forest ◽  
Age Estimation ◽  
The Body ◽  
Support Vector ◽  
Suitable Model ◽  
Estimation Model ◽  
Left Hand ◽  
Svm Model ◽  
Male Subjects ◽  
Female Subjects

In forensic anthropology, age estimation is used to ease the process of identifying the age of a living being or the body of a deceased person. Nonetheless, the specialty of the estimation models is solely suitable to a specific people. Commonly, the models are inter and intra-observer variability as the qualitative set of data is being used which results the estimation of age to rely on forensic experts. This study proposes an age estimation model by using length of bone in left hand of Asian subjects range from newborn up to 18-year-old. One soft computing model, which is Random Forest (RF) is used to develop the estimation model and the results are compared with Artificial Neural Network (ANN) and Support Vector Machine (SVM), developed in the previous case studies. The performance measurement used in this study and the previous case study are R-square and Mean Square Error (MSE) value. Based on the results produced, the RF model shows comparable results with the ANN and SVM model. For male subjects, the performance of the RF model is better than ANN, however less ideal than SVM model. As for female subjects, the RF model overperfoms both ANN and SVM model. Overall, the RF model is the most suitable model in estimating age for female subjects compared to ANN and SVM model, however for male subjects, RF model is the second best model compared to the both models. Yet, the application of this model is restricted only to experimental purpose or forensic practice.

The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques

2018 ◽  
Vol 7 (1) ◽  
pp. 72
Author(s):  
Khalid AA Abakar ◽  
Chongwen Yu
Keyword(s):  
Data Mining ◽  
Kernel Functions ◽  
Support Vector ◽  
Ann Model ◽  
Data Mining Techniques ◽  
Yarn Quality ◽  
Yarn Properties ◽  
Svm Model ◽  
Rbf Kernel

This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

MK-FSVM-SVDD: A Multiple Kernel-based Fuzzy SVM Model for Predicting DNA-binding Proteins via Support Vector Data Description

2020 ◽  
Vol 15 ◽  
Author(s):  
Yi Zou ◽  
Hongjie Wu ◽  
Xiaoyi Guo ◽  
Li Peng ◽  
Yijie Ding ◽  
...  
Keyword(s):  
Dna Binding ◽  
Binding Proteins ◽  
Detection Efficiency ◽  
Dna Binding Proteins ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Data Description ◽  
Multiple Kernel ◽  
Svm Model

Background: Detecting DNA-binding proetins (DBPs) based on biological and chemical methods is time consuming and expensive. Objective: In recent years, the rise of computational biology methods based on Machine Learning (ML) has greatly improved the detection efficiency of DBPs. Method: In this study, Multiple Kernel-based Fuzzy SVM Model with Support Vector Data Description (MK-FSVM-SVDD) is proposed to predict DBPs. Firstly, sex features are extracted from protein sequence. Secondly, multiple kernels are constructed via these sequence feature. Than, multiple kernels are integrated by Centered Kernel Alignment-based Multiple Kernel Learning (CKA-MKL). Next, fuzzy membership scores of training samples are calculated with Support Vector Data Description (SVDD). FSVM is trained and employed to detect new DBPs. Results: Our model is test on several benchmark datasets. Compared with other methods, MK-FSVM-SVDD achieves best Matthew's Correlation Coefficient (MCC) on PDB186 (0.7250) and PDB2272 (0.5476). Conclusion: We can conclude that MK-FSVM-SVDD is more suitable than common SVM, as the classifier for DNA-binding proteins identification.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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