Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar
This study established a Support Vector Machine (SVM)-based radar classification model of hydrometeor under the T-matrix based radar detection model of hydrometeors. Through normalizing data in the first place, it is also considered that data among polarization parameters are non-linear. Therefore,...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Torrential Rain and Disasters
2019-08-01
|
| Series: | 暴雨灾害 |
| Subjects: | |
| Online Access: | http://www.byzh.org.cn/cn/article/doi/10.3969/j.issn.1004-9045.2019.04.001 |
| _version_ | 1797786474009067520 |
|---|---|
| author | Tongxiao YANG Caijun YUE |
| author_facet | Tongxiao YANG Caijun YUE |
| author_sort | Tongxiao YANG |
| collection | DOAJ |
| description | This study established a Support Vector Machine (SVM)-based radar classification model of hydrometeor under the T-matrix based radar detection model of hydrometeors. Through normalizing data in the first place, it is also considered that data among polarization parameters are non-linear. Therefore, the study chose radial basis function as the kernel function of non-linear SVM and used Particle Swarm Optimization (PSO) to obtain the optimal kernel function parameters C and γ, so as to achieve higher accuracy of hydrometeor classification. The prediction accuracy of the established SVM-based radar classification model of hydrometeor reached more than 80% at X band and close to 95% at S band at all elevations. Further analysis shows that the prediction accuracy of hydrometeor classification can reach 97.3%, while the misjudgement is only 2.7%, when the prediction types of hydrometeor are the same with multi-wavelength joint observations. In conclusion, the establisehd SVM-based radar classification model of hydrometeor could improve both the ability to classify the hydrometeors in convective weather and the ability for early warning and forecasting of disastrous weather by dual linear polarization radars. |
| first_indexed | 2024-03-13T01:08:28Z |
| format | Article |
| id | doaj.art-0ea3379f4aad4bf286f8b98af782f0f0 |
| institution | Directory Open Access Journal |
| issn | 2097-2164 |
| language | zho |
| last_indexed | 2024-03-13T01:08:28Z |
| publishDate | 2019-08-01 |
| publisher | Editorial Office of Torrential Rain and Disasters |
| record_format | Article |
| series | 暴雨灾害 |
| spelling | doaj.art-0ea3379f4aad4bf286f8b98af782f0f02023-07-06T05:04:19ZzhoEditorial Office of Torrential Rain and Disasters暴雨灾害2097-21642019-08-0138429730210.3969/j.issn.1004-9045.2019.04.0012581Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radarTongxiao YANG0Caijun YUE1Shanghai Ecological Forecasting and Remote Sensing Center, Shanghai 200030Shanghai Ecological Forecasting and Remote Sensing Center, Shanghai 200030This study established a Support Vector Machine (SVM)-based radar classification model of hydrometeor under the T-matrix based radar detection model of hydrometeors. Through normalizing data in the first place, it is also considered that data among polarization parameters are non-linear. Therefore, the study chose radial basis function as the kernel function of non-linear SVM and used Particle Swarm Optimization (PSO) to obtain the optimal kernel function parameters C and γ, so as to achieve higher accuracy of hydrometeor classification. The prediction accuracy of the established SVM-based radar classification model of hydrometeor reached more than 80% at X band and close to 95% at S band at all elevations. Further analysis shows that the prediction accuracy of hydrometeor classification can reach 97.3%, while the misjudgement is only 2.7%, when the prediction types of hydrometeor are the same with multi-wavelength joint observations. In conclusion, the establisehd SVM-based radar classification model of hydrometeor could improve both the ability to classify the hydrometeors in convective weather and the ability for early warning and forecasting of disastrous weather by dual linear polarization radars.http://www.byzh.org.cn/cn/article/doi/10.3969/j.issn.1004-9045.2019.04.001dual linear polarization radarsupport vector machinemulti-wavelength joint observationconvective precipitation |
| spellingShingle | Tongxiao YANG Caijun YUE Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar 暴雨灾害 dual linear polarization radar support vector machine multi-wavelength joint observation convective precipitation |
| title | Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar |
| title_full | Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar |
| title_fullStr | Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar |
| title_full_unstemmed | Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar |
| title_short | Research on hydrometeor classification of convective weather based on SVM by dual linear polarization radar |
| title_sort | research on hydrometeor classification of convective weather based on svm by dual linear polarization radar |
| topic | dual linear polarization radar support vector machine multi-wavelength joint observation convective precipitation |
| url | http://www.byzh.org.cn/cn/article/doi/10.3969/j.issn.1004-9045.2019.04.001 |
| work_keys_str_mv | AT tongxiaoyang researchonhydrometeorclassificationofconvectiveweatherbasedonsvmbyduallinearpolarizationradar AT caijunyue researchonhydrometeorclassificationofconvectiveweatherbasedonsvmbyduallinearpolarizationradar |