| 引用本文: | 鲁铁定,李祯,贺小星.考虑噪声影响的MEMD-XGBoost方法在GNSS高程时间序列建模和预测中的应用.[J].国防科技大学学报,2024,46(6):149-158.[点击复制] |
| LU Tieding,LI Zhen,HE Xiaoxing.Noise-aware MEMD-XGBoost method for GNSS vertical time series modeling and prediction[J].Journal of National University of Defense Technology,2024,46(6):149-158[点击复制] |
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| 考虑噪声影响的MEMD-XGBoost方法在GNSS高程时间序列建模和预测中的应用 |
| 鲁铁定1,2,李祯1,贺小星3 |
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(1. 东华理工大学 测绘与空间信息工程学院, 江西 南昌 330013;2. 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;3. 江西理工大学 土木与测绘工程学院, 江西 赣州 341000)
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| 摘要: |
| 全球导航卫星系统(global navigation satellite system,GNSS)高程时间序列研究有助于监测和分析地壳板块运动,可以为研究人员判断区域运动趋势提供依据。基于经验模态分解和极端梯度提升算法构建了MEMD-XGBoost模型来预测分析GNSS高程时间序列。为了验证模型的预测性能,实验选取8个GNSS站高程时间序列数据进行预测实验,特征构造结果显示,多次经验模态分解可以准确地提取原始时间序列信息,提供有效特征。建模结果表明,MEMD-XGBoost模型可以有效改善数据质量。预测结果表明,MEMD-XGBoost模型预测结果具有较高的精度和准确率,误差离散程度较小,模型具有较强的稳定性和鲁棒性,可以较好地预测出GNSS站高程方向的运动趋势和季节性变化。因此,该模型可以应用于GNSS高程时间序列建模和预测研究。 |
| 关键词: GNSS时间序列 经验模态分解 极端梯度提升 建模 预测 |
| DOI：10.11887/j.cn.202406016 |
| 投稿日期：2022-06-30 |
| 基金项目:国家自然科学基金资助项目(42374040,42061077,42064001,42104023)；江西省自然科学基金资助项目(20202BABL213033;20202BAB212010)；江西理工大学高层次人才科研启动资助项目(205200100564) |
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| Noise-aware MEMD-XGBoost method for GNSS vertical time series modeling and prediction |
| LU Tieding1,2, LI Zhen1, HE Xiaoxing3 |
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(1. School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China;2. Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China;3. School of Civil Engineering and Surveying & Mapping Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China)
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| Abstract: |
| The study of GNSS(global navigation satellite system) vertical time series is helpful for monitoring and analyzing the movement of crustal plates, and can provide an important basis for judging the movement trend. A MEMD-XGBoost model was constructed based on empirical mode decomposition and extreme gradient boosting algorithm for GNSS vertical time series prediction and analysis. In order to verify the prediction performance of the model, the vertical time series data of 8 GNSS stations were selected for prediction experiments. The feature construction results show that multiple empirical mode decomposition can accurately extract the original time series information and provide effective features. The modeling results show that the MEMD-XGBoost model can effectively improve the data quality. The prediction results show that the prediction results of the MEMD-XGBoost model have high precision and accuracy, and the degree of error dispersion is small, the model has strong stability and robustness, and can better predict the movement trend and seasonal changes in the U direction of the GNSS station. Therefore, the model can be applied to GNSS vertical time series modeling and prediction research. |
| Keywords: GNSS time series empirical mode decomposition extreme gradient boosting modeling prediction |
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