Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region
In this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velo...
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/9/3086 |
| _version_ | 1797569409972174848 |
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| author | Dongwang Tao Qiang Ma Shuilong Li Zhinan Xie Dexin Lin Shanyou Li |
| author_facet | Dongwang Tao Qiang Ma Shuilong Li Zhinan Xie Dexin Lin Shanyou Li |
| author_sort | Dongwang Tao |
| collection | DOAJ |
| description | In this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity, Housner intensity, acceleration spectrum intensity, velocity spectrum intensity, and others, are considered as candidates for feature selection to generate optimal SVR models. The datasets with both useable strong ground motion records and corresponding investigated MSIs in the Sichuan–Yunnan region, China, are all collected, and these 125 pairs of datasets are used for selecting features and comparing regression results. Nine ground motion parameters are selected as the most relevant features: PGA is the first fundamental one and PGV is the fifth relevant feature. Based on performance measures on the testing dataset, the best SVR model is given when the number of features is one all the way up to nine. According to predicted accuracy, SVR models with Gaussian kernel give much better MSI prediction than linear kernel SVR models and linear regression models. In particular, the Gaussian kernel SVR of PGA gives much higher MSI prediction accuracy than the linear regression model of PGV and PGA. The proposed SVR models are valid for MSI values from VI to IX, and they can be used for rapid mapping damage potential and reporting seismic intensity for this high-seismic-activity region. |
| first_indexed | 2024-03-10T20:10:22Z |
| format | Article |
| id | doaj.art-4ce516d1118146c89a405952bc74db5e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:10:22Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-4ce516d1118146c89a405952bc74db5e2023-11-19T22:57:40ZengMDPI AGApplied Sciences2076-34172020-04-01109308610.3390/app10093086Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan RegionDongwang Tao0Qiang Ma1Shuilong Li2Zhinan Xie3Dexin Lin4Shanyou Li5Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaEarthquake Administration of Fujian Province, Fuzhou 350003, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaIn this paper, a nonlinear regression method called a support vector regression (SVR) is presented to establish the relationship between engineering ground motion parameters and macroseismic intensity (MSI). Sixteen ground motion parameters, including peak ground acceleration (PGA), peak ground velocity (PGV), Arias intensity, Housner intensity, acceleration spectrum intensity, velocity spectrum intensity, and others, are considered as candidates for feature selection to generate optimal SVR models. The datasets with both useable strong ground motion records and corresponding investigated MSIs in the Sichuan–Yunnan region, China, are all collected, and these 125 pairs of datasets are used for selecting features and comparing regression results. Nine ground motion parameters are selected as the most relevant features: PGA is the first fundamental one and PGV is the fifth relevant feature. Based on performance measures on the testing dataset, the best SVR model is given when the number of features is one all the way up to nine. According to predicted accuracy, SVR models with Gaussian kernel give much better MSI prediction than linear kernel SVR models and linear regression models. In particular, the Gaussian kernel SVR of PGA gives much higher MSI prediction accuracy than the linear regression model of PGV and PGA. The proposed SVR models are valid for MSI values from VI to IX, and they can be used for rapid mapping damage potential and reporting seismic intensity for this high-seismic-activity region.https://www.mdpi.com/2076-3417/10/9/3086macroseismic intensityground motion parameterssupport vector regressionSichuan–Yunnan regionlinear regressionPGA |
| spellingShingle | Dongwang Tao Qiang Ma Shuilong Li Zhinan Xie Dexin Lin Shanyou Li Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region Applied Sciences macroseismic intensity ground motion parameters support vector regression Sichuan–Yunnan region linear regression PGA |
| title | Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region |
| title_full | Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region |
| title_fullStr | Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region |
| title_full_unstemmed | Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region |
| title_short | Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region |
| title_sort | support vector regression for the relationships between ground motion parameters and macroseismic intensity in the sichuan yunnan region |
| topic | macroseismic intensity ground motion parameters support vector regression Sichuan–Yunnan region linear regression PGA |
| url | https://www.mdpi.com/2076-3417/10/9/3086 |
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