Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification
The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven exp...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
|
| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/459137 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832562235809988608 |
|---|---|
| author | Longjun Dong Xibing Li Gongnan Xie |
| author_facet | Longjun Dong Xibing Li Gongnan Xie |
| author_sort | Longjun Dong |
| collection | DOAJ |
| description | The discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. The result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. The discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights. |
| format | Article |
| id | doaj-art-05711c9ad6ee4722b8ee42cddabe2448 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-05711c9ad6ee4722b8ee42cddabe24482025-02-03T01:23:05ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/459137459137Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes ClassificationLongjun Dong0Xibing Li1Gongnan Xie2School of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha 410083, ChinaSchool of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, 710129, ChinaThe discrimination of seismic event and nuclear explosion is a complex and nonlinear system. The nonlinear methodologies including Random Forests (RF), Support Vector Machines (SVM), and Naïve Bayes Classifier (NBC) were applied to discriminant seismic events. Twenty earthquakes and twenty-seven explosions with nine ratios of the energies contained within predetermined “velocity windows” and calculated distance are used in discriminators. Based on the one out cross-validation, ROC curve, calculated accuracy of training and test samples, and discriminating performances of RF, SVM, and NBC were discussed and compared. The result of RF method clearly shows the best predictive power with a maximum area of 0.975 under the ROC among RF, SVM, and NBC. The discriminant accuracies of RF, SVM, and NBC for test samples are 92.86%, 85.71%, and 92.86%, respectively. It has been demonstrated that the presented RF model can not only identify seismic event automatically with high accuracy, but also can sort the discriminant indicators according to calculated values of weights.http://dx.doi.org/10.1155/2014/459137 |
| spellingShingle | Longjun Dong Xibing Li Gongnan Xie Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification Abstract and Applied Analysis |
| title | Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification |
| title_full | Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification |
| title_fullStr | Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification |
| title_full_unstemmed | Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification |
| title_short | Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification |
| title_sort | nonlinear methodologies for identifying seismic event and nuclear explosion using random forest support vector machine and naive bayes classification |
| url | http://dx.doi.org/10.1155/2014/459137 |
| work_keys_str_mv | AT longjundong nonlinearmethodologiesforidentifyingseismiceventandnuclearexplosionusingrandomforestsupportvectormachineandnaivebayesclassification AT xibingli nonlinearmethodologiesforidentifyingseismiceventandnuclearexplosionusingrandomforestsupportvectormachineandnaivebayesclassification AT gongnanxie nonlinearmethodologiesforidentifyingseismiceventandnuclearexplosionusingrandomforestsupportvectormachineandnaivebayesclassification |