在基于虚拟样本生成和高斯过程回归的有限监测数据的情况下,改善地下水质量的预测

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Jiang Zhang1, Changlai Xiao1, Weifei Yang1

  • 1Key Laboratory of Groundwater Resources and Environment, Ministry of Education, Jilin University Changchun 130021, China; Jilin Provincial Key Laboratory of Water Resources and Environment, Jilin University Changchun 130021, China; College of New Energy and Environment, Jilin University, Changchun 130021, China; National-Local Joint Engineering Laboratory of In-Situ Conversion, Drilling and Exploitation Technology for Oil Shale, Changchun 130021, China.

Water research
|September 27, 2024
PubMed
概括

虚拟样本生成增强了高斯过程回归,用于在有限数据的情况下预测水质. 以t分布式的随机近邻嵌入方法显著提高了预测水平的准确性.

关键词:
在GPR中使用GPR.生成性的对抗性网络.在 MD-MTD 之间.地下水中的.在VSG中,VSG是VSG.t 分布的随机邻居嵌入.

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