Support Vector Regression for the Relationships between Ground Motion Parameters and Macroseismic Intensity in the Sichuan–Yunnan Region

Abstract

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.

A Novel Approach to Air Passenger Index Prediction: Based on Mutual Information Principle and Support Vector Regression Blended Model

“…The support vector regression (SVR) is a widely used ML prediction method, which adopts the principle of minimizing structural risk rather than minimizing empirical risk. This allows one to effectively mitigate numerous problems, such as "dimensional disaster" and traditional pattern recognition (Farber et al, 2016;Li, Ni, et al, 2018;Nieto et al, 2013;Qingyang et al, 2012;Sun et al, 2011Sun et al, , 2014Tao et al, 2020;Yang et al, 2017). The general linear regression model can be expressed as follows:…”

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-SVR) Blended Model

“…The support vector regression (SVR) is a widely used ML prediction method, which adopts the principle of minimizing structural risk rather than minimizing empirical risk. This allows one to effectively mitigate numerous problems, such as "dimensional disaster" and traditional pattern recognition [17][18][19][32][33][34][35][36]. The general linear regression model can be expressed as follows：…”

An Integrated Approach to Air Passenger Index Prediction: Mutual Information Principle and Support Vector Regression (MI-Svr) Blended Model

“…recognition [17][18][19][32][33][34][35][36]. The general linear regression model can be expressed as follows：…”