顾及地球物理效应的GNSS高程时间序列AdaBoost预测和插值方法
Prediction and interpolation of GNSS vertical time series based on the AdaBoost method considering geophysical effects
鲁铁定 1李祯2
作者信息
- 1. 东华理工大学测绘与空间信息工程学院,江西南昌 330013;自然资源部环鄱阳湖区域矿山环境监测与治理重点实验室,江西南昌 330013
- 2. 东华理工大学测绘与空间信息工程学院,江西南昌 330013
- 折叠
摘要
传统的GNSS高程时间序列预测和插值方法仅考虑时间变量,具有明显的局限性.本文顾及地球物理效应的影响,通过温度、大气压强、极移等数据和GNSS高程时间序列数据构建回归问题,使用自适应提升(AdaBoost)算法建模.为了验证模型的预测和插值性能,试验选取4个GNSS站的高程时间序列进行分析.建模试验表明,相较于Prophet模型,AdaBoost模型的拟合精度提升了约35％;预测结果表明,在12个月的预测周期内,AdaBoost模型在4个GNSS站的MAE值为4.0～4.5 mm,RMSE值约为5.0～6.0 mm;插值试验表明,相较于三次样条插值方法,AdaBoost插值模型的精度约提升了 15％-28％.预测和插值试验表明,顾及地球物理效应的AdaBoost模型可以应用于GNSS高程时间序列预测与插值.
Abstract
Traditional GNSS vertical time series prediction and interpolation methods only consider time variables and have ob-vious limitations.This study takes into account the impact of geophysical effects and constructs a regression problem using temperature,atmospheric pressure,polar motion,and GNSS vertical time series data,uses the adaptive boost(AdaBoost)algorithm for modeling.To verify the prediction and interpolation performance of the model,the vertical time series from 4 GNSS stations were selected for analysis.The modeling experiment shows that compared to the Prophet model,the fitting accuracy of AdaBoost model has been improved by 35％.The prediction results indicate that within a 12 month prediction peri-od,the MAE values of the AdaBoost model at four GNSS stations are approximately 4.0～4.5 mm,and the RMSE values are approximately 5.0～6.0 mm.The interpolation experiment shows that compared to the cubic spline interpolation method,the accuracy of AdaBoost interpolation model has been improved by about 15％-28％.Our experiments have shown that the Ada-Boost model considering geophysical effects can be applied to the prediction and interpolation of GNSS vertical time series.
关键词
GNSS高程时间序列/地球物理效应/预测/插值/自适应提升算法Key words
GNSS vertical time series/geophysical effects/prediction/interpolation/adaptive boosting algorithm引用本文复制引用
基金项目
国家自然科学基金(42061077)
国家自然科学基金(42374040)
出版年
2024
NETL
NSTL
万方数据