灾害学2024，Vol.39Issue(4)：40-46,7.DOI:10.3969/j.issn.1000-811X.2024.04.006
基于GAN的地震人员死亡样本扩充方法研究
Research on GAN-based Sample Expansion Method for Earthquake Personnel Deaths
摘要
Abstract
Earthquake is a natural disaster with great destructiveness,which seriously threatens the social and eco-nomic development.Accurate prediction of the number of deaths at the first time after an earthquake is of great significance for disaster relief and emergency response.However,due to the limited number of historical earthquake samples,it is diffi-cult to ensure the stability of small sample models.In this paper,GAN is used to expand the samples to obtain an augment-ed dataset that is highly similar to the original dataset.The empirical model is then fitted using the two datasets,and the training and prediction performance of the machine learning models(SVR,ELM,BP,DNN)are also compared.The re-sults show that the augmented dataset not only enhances the fitting effect of the empirical models,but also has a significant effect on the prediction performance of the machine learning models,and the root-mean-square error between the predicted and true values of each model is reduced by an average of 48.37%after the sample size expansion compared to the pre-expansion period.Therefore,the augmented dataset obtained by GAN expansion can be used as an effective supplement to the original samples,and is an effective way to improve the accuracy of the earthquake personnel death model.关键词
地震人员死亡/生成对抗网络/小样本/样本扩充/机器学习Key words
earthquake fatalities/generative adversarial networks/small samples/sample expansion/machine learning分类
资源环境引用本文复制引用
赵煜,李娅妮,孙艳萍,史一彤,陈文凯..基于GAN的地震人员死亡样本扩充方法研究[J].灾害学,2024,39(4):40-46,7.基金项目
国家社科基金西部项目"生态安全视阈内黄河上游城市群韧性测度及优化路径研究"(21XTJ004) （21XTJ004）
兰州财经大学重点项目"兰州—西宁城市群高质量协同发展机制、测度与提升路径研究"(Lzufe2022B-005) （Lzufe2022B-005）
