Investigation of a Semi-FE Approach to Prediction of Floor Response to Walking

Nguyen, Huu Anh Tuan

doi:10.1007/978-981-16-7160-9_33

Cited by 1 publication

(1 citation statement)

References 3 publications

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“…Ensemble models can alleviate the underfitting or overfitting caused by a single model so as to improve the generalization ability and robustness of the model. As a result, XGBoost has been applied to a wide spectrum of problems, such as prediction of air pollutant (Ma et al 2020;Li et al 2022), oil prospecting (Markovic et al 2022), economic and financial forecasting (Li et al 2021), prediction of the virus spread (Fang et al 2022), road and bridge engineering (Li et al 2020;Le Nguyen 2022), and so on.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Transit Time of Coronal Mass Ejections with an Ensemble Machine-learning Method

Yang,

Liu,

Feng

et al. 2023

ApJS

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Coronal mass ejections (CMEs), a kind of violent solar eruptive activity, can exert a significant impact on space weather. When arriving at the Earth, they interact with the geomagnetic field, which can boost the energy supply to the geomagnetic field and may further result in geomagnetic storms, thus having potentially catastrophic effects on human activities. Therefore, accurate forecasting of the transit time of CMEs from the Sun to the Earth is vital for mitigating the relevant losses brought by them. XGBoost, an ensemble model that has better performance in some other fields, is applied to the space weather forecast for the first time. During multiple tests with random data splits, the best mean absolute error (MAE) of ∼5.72 hr was obtained, and in this test, 62% of the test CMEs had absolute arrival time error of less than 5.72 hr. The average MAE over all random tests was ∼10 hr. It indicates that our method has a better predictive potential and baseline. Moreover, we introduce two effective feature importance ranking methods. One is the information gain method, a built-in method of ensemble models. The other is the permutation method. These two methods combine the learning process of the model and its performance to rank the CME features, respectively. Compared with the direct correlation analysis on the sample data set, they can help select the important features that closely match the model. These two methods can assist researchers to process large sample data sets, which often require feature selection in advance.

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