Kunming zenith wet delay model based on a backpropagation neural network

doi:10.13474/j.cnki.11-2246.2022.0086

Abstract

Abstract: For the high-precision zenith wet delay (ZWD) used in Kunming continuously operating reference stations (KMCORS),this paper developes the Kunming model (KM) suitable for the KM area.According to the sounding data of the KM sounding station from 2015 to 2018,the KM model is generated based on a backpropagation (BP) neural network.This study then validates the prediction performance of the KM model using the sounding data during 2019.Test results show that the RMSE of the KM model decreases from 4.0 cm to 2.2 cm compared with the widely used Saastamoninen (SA) model,indicating its 45% accuracy improvement.Additionally,the Bias of the KM and SA models are 0 and-3.1 cm,respectively,suggesting that the ZWD estimation of the KM model is unbiased,while the SA model has the problem of overestimation in the plateau area.In summary,the KM model has better prediction performance than the SA empirical model,and the application of the KM model will help to improve the service quality of KMCORS.

Key words: Kunming CORS;zenith wet delay;backpropagation neural network;Saastamoninen model

CLC Number:

P228.4

DING Renjun, WANG Youkun, ZHANG Junhua, LIU Chen. Kunming zenith wet delay model based on a backpropagation neural network[J]. Bulletin of Surveying and Mapping, 2022, 0(3): 107-110.

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