Residual Subsidence Prediction of Abandoned Mine Goaf Based on Wavelet Support Vector Machines

2012 ◽  
Vol 524-527 ◽  
pp. 330-336 ◽  
Author(s):  
Zheng Shuai Wang ◽  
Ka Zhong Deng
Keyword(s):  
Support Vector Machines ◽  
Prediction Model ◽  
Abandoned Mine ◽  
Support Vector ◽  
Stochastic Fluctuation ◽  
Stochastic Signal ◽  
Multi Resolution Analysis ◽  
Svm Model ◽  
Vector Machines ◽  
Resolution Analysis

The prediction of residual subsidence is the fundament of stability evaluation of buildings foundation in the abandoned mine goaf, so how to get the residual subsidence with high precision is significant to reclaim the goaf for buildings. In this paper, a novel prediction model named wavelet support vector machines (WT-SVM) is proposed to forecast residual subsidence. Aiming at the stochastic fluctuation of the subsidence series, the measured data of residual subsidence were separated into components, namely, trend, oscillating sequence and stochastic signal, via wavelet multi-resolution analysis; then, the prediction model was established based on SVM regression algorithm, respectively, and the sum of the total corresponding prediction values were regarded as the final results of the residual subsidence. The predicting results of WT-SVM, SVM and BP neural network (BP-NN) were compared by a case study. The conclusions are as follows: WT-SVM model is obviously superior to other models in terms of the aspects of prediction precision, step and stability, which indicates the feasibility and effectivity of WT-SVM in predicting residual subsidence of the abandoned mine goaf.

scholarly journals Intelligent Fault Diagnosis of Bearings Based on Energy Levels in Frequency Bands Using Wavelet and Support Vector Machines (SVM)

2019 ◽  
Vol 3 (1) ◽  
pp. 11 ◽  
Author(s):  
Seyed Yadavar Nikravesh ◽  
Hossein Rezaie ◽  
Margaret Kilpatrik ◽  
Hossein Taheri
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Wavelet Packet ◽  
Energy Levels ◽  
Support Vector ◽  
Frequency Bands ◽  
Multi Resolution Analysis ◽  
Vector Machines ◽  
Resolution Analysis

In this paper, a new method was introduced for feature extraction and fault diagnosis in bearings based on wavelet packet decomposition and analysis of the energy in different frequency bands. This method decomposes a signal into different frequency bands using different types of wavelets and performs multi-resolution analysis to extract different features of the signals by choosing energy levels in different frequency bands. The support vector machines (SVM) technique was used for faults classifications. Daubechies, biorthogonal, coiflet, symlet, Meyer, and reverse Meyer wavelets were used for feature extraction. The most appropriate decomposition level and frequency band were selected by analyzing the variation in the signal’s energy level. The proposed approach was applied to the fault diagnosis of rolling bearings, and testing results showed that the proposed approach can reliably identify different fault categories and their severities. Moreover, the effectiveness of the proposed feature selection and fault diagnosis method was significant based on the similarity between the wavelet packet and the signal, and effectively reduced the influence of the signal noise on the classification results.

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.

scholarly journals Forecasting Electric Load by Support Vector Machines with Genetic Algorithms

2005 ◽  
Vol 9 (2) ◽  
pp. 134-141 ◽  
Author(s):  
Ping-Feng Pai ◽  
◽  
Wei-Chiang Hong ◽  
Chih-Shen Lin ◽  
◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Moving Average ◽  
Predictive Performance ◽  
Support Vector ◽  
Electric Load ◽  
Power Company ◽  
Svm Model ◽  
Vector Machines ◽  
General Regression Neural Networks

Support vector machines (SVMs) have been successfully used in solving nonlinear regression and time series problems. However, the application of SVMs to load forecasting is very rare. Therefore, the purpose of this paper is to examine the feasibility of SVMs in forecasting electric load. In addition, the genetic algorithms are applied in the parameter selection of SVM model. Forecasting results compared with other two models, namely autoregressive integrated moving average (ARIMA) and general regression neural networks (GRNN), are provided. The experimental data are borrowed from the Taiwan Power Company. The numerical results indicate that the SVM model with genetic algorithms (SVMG) results in better predictive performance than the other two approaches.

scholarly journals Faster Support Vector Machines

2021 ◽  
Vol 26 (1) ◽  
pp. 1-21
Author(s):  
Sebastian Schlag ◽  
Matthias Schmitt ◽  
Christian Schulz
Keyword(s):  
Support Vector Machines ◽  
Time Complexity ◽  
Original Problem ◽  
Label Propagation ◽  
Support Vector ◽  
Propagation Algorithm ◽  
Svm Model ◽  
Vector Machines ◽  
Data Points ◽  
Classification Quality

The time complexity of support vector machines (SVMs) prohibits training on huge datasets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge datasets. While regular SVMs perform the entire training in one—time-consuming—optimization step, multilevel SVMs first build a hierarchy of problems decreasing in size that resemble the original problem and then train an SVM model for each hierarchy level, benefiting from the solved models of previous levels. We present a faster multilevel support vector machine that uses a label propagation algorithm to construct the problem hierarchy. Extensive experiments indicate that our approach is up to orders of magnitude faster than the previous fastest algorithm while having comparable classification quality. For example, already one of our sequential solvers is on average a factor 15 faster than the parallel ThunderSVM algorithm, while having similar classification quality. 1

scholarly journals LAND COVER CLASSIFICATION FROM FULL-WAVEFORM LIDAR DATA BASED ON SUPPORT VECTOR MACHINES

2016 ◽  
Vol XLI-B3 ◽  
pp. 447-452
Author(s):  
M. Zhou ◽  
C. R. Li ◽  
L. Ma ◽  
H. C. Guan
Keyword(s):  
Support Vector Machines ◽  
Land Cover ◽  
Prediction Model ◽  
Image Data ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Waveform Lidar

In this study, a land cover classification method based on multi-class Support Vector Machines (SVM) is presented to predict the types of land cover in Miyun area. The obtained backscattered full-waveforms were processed following a workflow of waveform pre-processing, waveform decomposition and feature extraction. The extracted features, which consist of distance, intensity, Full Width at Half Maximum (FWHM) and back scattering cross-section, were corrected and used as attributes for training data to generate the SVM prediction model. The SVM prediction model was applied to predict the types of land cover in Miyun area as ground, trees, buildings and farmland. The classification results of these four types of land covers were obtained based on the ground truth information according to the CCD image data of Miyun area. It showed that the proposed classification algorithm achieved an overall classification accuracy of 90.63%. In order to better explain the SVM classification results, the classification results of SVM method were compared with that of Artificial Neural Networks (ANNs) method and it showed that SVM method could achieve better classification results.

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