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

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.

Load Forecasting of Coal-Fired Unit Based on SVM Model

2012 ◽  
Vol 466-467 ◽  
pp. 1015-1019 ◽  
Author(s):  
Wei Liang Liu ◽  
Yong Guang Ma ◽  
Liang Yu Ma ◽  
Yong Jun Lin ◽  
Shuang Sai Liu
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecasting Accuracy ◽  
Svm Model ◽  
Bp Neural Network Model

In order to obtain accurate load forecasting of coal-fired unit, a new algorithm based on Support Vector Machine (SVM) method is presented. This algorithm establishes a model to reflect the complicated relation between the load of coal-fired unit and the furnace flame Images. The trained SVM model is applied to a 660MW coal-fired unit to forecast the load with two groups of test samples. The results are compared with that of BP neural network model. It is shown the SVM model is more accurate than the BP NN model. The SVM method can satisfy the demand of engineering applications with the advantages of high forecasting accuracy and more generalized performance.

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.

Thermal–Mechanical Coupling Evaluation of the Panel Performance of a Prefabricated Cabin-Type Substation Based on Machine Learning

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 93
Author(s):  
Xiangsheng Lei ◽  
Jinwu Ouyang ◽  
Yanfeng Wang ◽  
Xinghua Wang ◽  
Xiaofeng Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Numerical Simulation ◽  
Fire Resistance ◽  
Bp Neural Network ◽  
Stress Test ◽  
Normal Operation ◽  
Model Parameters ◽  
Mechanical Coupling ◽  
The Impact

The panel performance of a prefabricated cabin-type substation under the impact of fires plays a vital role in the normal operation of the substation. However, current evaluations of the panel performance of substations under fire still focus on fire resistance tests, which seldom consider the relationship between fire behavior and the mechanical load of the panel under the impact of fires. Aiming at the complex and uncertain relationship between the thermal and mechanical performance of the substation panel under impact of fires, this paper proposes a machine learning method based on a BP neural network. First, the fire resistance test and the stress test of the panel is carried out, then a machine learning model is established based on the BP neural network. According to the collected data, the model parameters are obtained through a series of training and verification processes. Meanwhile, the correlation between the panel performance and fire resistance was obtained. Finally, related parameters are input into the thermal–mechanical coupling evaluation model for the substation panel performance to evaluate the fire resistance performance of the substation panel. To verify the correctness of the established model, numerical simulation of the fire test and stress test of the panel is conducted, and numerical simulation samples are predicted by the trained model. The results show that the prediction curve of neural network is closer to the real results compared with the numerical simulation, and the established model can accurately evaluate the thermal–mechanical coupling performance of the substation panel under fire.

SVM MODELS FOR DIAGNOSING BALANCE PROBLEMS USING STATISTICAL FEATURES OF THE MTC SIGNAL

2008 ◽  
Vol 07 (03) ◽  
pp. 317-331 ◽  
Author(s):  
DANIEL T. H. LAI ◽  
REZAUL BEGG ◽  
MARIMUTHU PALANISWAMI
Keyword(s):  
Feature Selection Method ◽  
Gaussian Kernel ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Model Parameters ◽  
Statistical Features ◽  
Gait Patterns ◽  
Healthy Elderly ◽  
Svm Model ◽  
Balance Problems

Trip-related falls are a major problem in the elderly population and research in the area has received much attention recently. The focus has been on devising ways of identifying individuals at risk of sustaining such falls. The main aim of this work is to explore the effectiveness of models based on Support Vector Machines (SVMs) for the automated recognition of gait patterns that exhibit falling behavior. Minimum toe clearance (MTC) during continuous walking on a treadmill was recorded on 10 healthy elderly and 10 elderly with balance problems and with a history of tripping falls. Statistical features obtained from MTC histograms were used as inputs to the SVM model to classify between the healthy and balance-impaired subjects. The leave-one-out technique was utilized for training the SVM model in order to find the optimal model parameters. Tests were conducted with various kernels (linear, Gaussian and polynomial) and with a change in the regularization parameter, C, in an effort to identify the optimum model for this gait data. The receiver operating characteristic (ROC) plots of sensitivity and specificity were further used to evaluate the diagnostic performance of the model. The maximum accuracy was found to be 90% using a Gaussian kernel with σ2 = 10 and the maximum ROC area 0.98 (80% sensitivity and 100% specificity), when all statistical features were used by the SVM models to diagnose gait patterns of healthy and balance-impaired individuals. This accuracy was further improved by using a feature selection method in order to reduce the effect of redundant features. It was found that two features (standard deviation and maximum value) were adequate to give an improved accuracy of 95% (90% sensitivity and 100% specificity) using a polynomial kernel of degree 2. These preliminary results are encouraging and could be useful not only for diagnostic applications but also for evaluating improvements in gait function in the clinical/rehabilitation contexts.

Prediction of Coal Production Based on BP Neural Network

2011 ◽  
Vol 217-218 ◽  
pp. 1647-1651
Author(s):  
Ming Ming Wen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Historical Data ◽  
Supply And Demand ◽  
Simulation Software ◽  
Coal Production ◽  
Matlab Simulation ◽  
Coal Supply

It is significance to predict coal production for balancing coal supply and demand in China. The primary goal of this research is the prediction of coal production in china. The method used in the study is known as the BP neural network. The BP neural network is designed with the MATLAB simulation software based on coal production historical data from 1980 to 2007. The studies we have performed showed that the prediction of coal production based on BP neural network is reasonable and valuable. Finally, we get the prediction of coal production from 2010 to 2015, and the prediction indicates that the coal production will increase in the next 5 years.

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