The prediction of the time-series PS-InSAR ground subsidence based on gated recurrent neural network
To prevent damage to infrastructure caused by ground subsidence caused by land reclamation at Hong Kong International Airport,China,this paper used the Persistent Scatterer Interferometric Synthetic Aperture Radar(PS-InSAR)technique to obtain ground subsidence data for 2016-2020 at Hong Kong International Airport,China.We used the Small Baseline Subset Interferometric Synthetic Aperture Radar(SBAS-InSAR)technology and level data to verify the results of PS-InSAR.Moreover,Gated Recurrent Unit(GRU)neural network was introduced to construct a stacked GRU ground subsidence prediction model,and the future ground subsidence of Hong Kong International Airport in China was predicted in time-series,and compared with Support Vector Machine(SVM)and Multilayer Perceptron(MLP)neural network.The results showed that the spatial distribution of ground subsidence at Hong Kong International Airport,China,from 2016 to 2020 was uneven,the accumulated subsidence gradually increased,and the accumulated subsidence in the vertical direction in December 2020 reached 106 mm.The constructed stacked GRU neural network ground subsidence method was more accurate than SVM and MLP,and the maximum accumulated ground subsidence at Hong Kong International Airport,China,in July 2021 reached 111.8 mm.The ground subsidence time series prediction model proposed in this paper can be used as an effective method to predict ground subsidence and provide key technical support for early warning of ground subsidence.
国家科技期刊平台
NSTL
万方数据
