基于ConvLSTM网络的区域对流层湿延迟预报方法

doi:10.11947/j.AGCS.2025.20240108

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 795-804.doi: 10.11947/j.AGCS.2025.20240108

基于ConvLSTM网络的区域对流层湿延迟预报方法

范昊鹏¹^,²(), 张博骄², 孙中苗³, 冯进凯²

^1.湖北珞珈实验室，湖北　武汉　430075
^2.信息工程大学地理空间信息学院，河南　郑州　450001
^3.西安测绘研究所，陕西　西安　710054

收稿日期:2024-03-19 修回日期:2025-05-21 出版日期:2025-06-23 发布日期:2025-06-23
作者简介:范昊鹏（1989—），男，博士，副教授，研究方向为时空数据智能处理、海洋测绘。E-mail：362158438@qq.com
基金资助:
湖北珞珈实验室开放基金(230100032);河南省自然科学基金(232300421403);国家自然科学基金(42174001)

Prediction method of regional tropospheric wet delay based on Conv-LSTM network

Haopeng FAN¹^,²(), Bojiao ZHANG², Zhongmiao SUN³, Jinkai FENG²

^1.Hubei Luojia Laboratory, Wuhan 430075, China
^2.Institute of Surveying and Mapping, Information Engineering University, Zhengzhou 450001, China
^3.Xi'an Institute of Surveying and Mapping, Xi'an 710054, China

Received:2024-03-19 Revised:2025-05-21 Online:2025-06-23 Published:2025-06-23
About author:FAN Haopeng (1989—), male, PhD, associate professor, majors in intelligent processing of spatio-temporal data and marine surveying.　E-mail: 362158438@qq.com
Supported by:
Open Fund of Hubei Luojia Laboratory(230100032);Natural Science Foundation of Henan Province(232300421403);The National Natural Science Foundation of China(42174001)

摘要/Abstract

摘要：

对流层湿延迟（ZWD）时变性强，随地理位置而变，现已成为制约各类空间大地测量技术精度或时效性的主要瓶颈之一。鉴于此，本文基于卷积长短期记忆（ConvLSTM）网络，将区域ZWD的历史时空序列向上延拓，以增强数据的空间相关性，同时采用增量训练的方式，提高时空序列对突变信号的注意力；最后，本文以中欧地区为例，对比了滑动窗口二次曲线外推、经典ConvLSTM及本文方法的预报效果。结果表明，滑动窗口二次曲线法的短期预报精度与经典ConvLSTM方法相当，当预报跨度增大后前者精度骤降，而后者相比之下几乎不受影响；在使用增量改进方法后，预报精度在经典ConvLSTM方法基础上提高了60%；在使用“延拓+增量”改进方法后，预报的系统误差进一步降低了50%以上。

关键词: 空间大地测量, 对流层湿延迟, 时空序列预测, 机器学习, 增量学习

Abstract:

The tropospheric zenith wet delay (ZWD) is time-varying and varies with geographical locations, which has become one of the main bottlenecks restricting the accuracy or timeliness of various spatial geodetic technologies. In view of this, a prediction method based on convolutional long-short term memory (ConvLSTM) network was exploited, during which a continuation of regional historical ZWD was conducted to enhance the spatial correlation, and an incremental training was adopted to improve the attention of spatio-temporal series to sudden changing signals; finally, taking the central European region as an example, the calculation effects of the sliding window conic extrapolation, the classical ConvLSTM and the method in this paper were compared. The results show that the short-term accuracy of the sliding window conic method is equivalent to that of the classical ConvLSTM; yet, when the prediction span increases, the accuracy of the former decreases sharply, while the latter is almost unaffected. After using the incremental improvement method, the accuracy is improved by 60% on the basis of the classical ConvLSTM method; after employing the “extension+increment” method, the systematic error is even further reduced by more than 50%.

Key words: space geodesy, tropospheric wet delay, prediction of spatio-temporal series, machine learning, incremental learning

中图分类号:

P228

范昊鹏, 张博骄, 孙中苗, 冯进凯. 基于ConvLSTM网络的区域对流层湿延迟预报方法[J]. 测绘学报, 2025, 54(5): 795-804.

Haopeng FAN, Bojiao ZHANG, Zhongmiao SUN, Jinkai FENG. Prediction method of regional tropospheric wet delay based on Conv-LSTM network[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 795-804.

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ConvLSTM参数	设置情况	备注
输入矩阵尺寸	41×81×7	前两维为ZWD格网尺寸，第三维为历史步长
输出矩阵尺寸	41×81×7	前两维为ZWD格网尺寸，第三维为预测步长
训练数据样本	前480个历元	—
测试数据样本	后240个历元	—
卷积核尺寸	3×3	—
激活函数	ReLU	—
优化器	Adam	—
损失函数	MSE	—
循环训练次数	50	—
时空增量策略	方法1：经典方法	方法1：无特殊设置
	方法2：相邻历元作差	方法2：利用相邻历元差值作为ConvLSTM的输入信息
	方法3：延拓+相邻历元作差	方法3：将区域ZWD向上延拓至区域内最高点所在平面（根据试验区地形数据，最高点海拔为3114 m），作差后再输入ConvLSTM网络，对预测后的结果再向下延拓至地表

方法	预报步长	最小值/mm	最大值/mm	平均值/mm	标准差/mm
滑动窗口二次曲线	1	1.4	12.0	4.7	2.1
	3	5.1	38.1	15.6	6.8
	5	9.9	72.1	30.3	12.9
	7	17.0	116.6	49.0	20.5
经典ConvLSTM	1	0.8/2.1	4.4/12.8	1.8/5.5	0.7/1.9
	3	2.4/1.8	11.9/9.2	5.6/4.2	2.3/1.4
	5	3.5/2.6	18.0/12.0	8.0/6.6	3.3/2.2
	7	4.5/4.9	22.7/18.9	10.1/10.7	4.0/3.4

基于ConvLSTM网络的区域对流层湿延迟预报方法

Prediction method of regional tropospheric wet delay based on Conv-LSTM network

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