Prediction of Urban Surface Deformation by Integrating Time-Series InSAR and Spatiotemporal Dual-Driven Deep Learning

Name: Prediction of Urban Surface Deformation by Integrating Time-Series InSAR and Spatiotemporal Dual-Driven Deep Learning
Creator: Hongguo Jia
Published: 2026-03-18T16:46:59.084Z
Keywords: Geography, Machine Learning

Jia, Hongguo; Zhang, Cuilan; Liu, Yuchen

doi:10.17632/6szg7mcbw2.1

Prediction of Urban Surface Deformation by Integrating Time-Series InSAR and Spatiotemporal Dual-Driven Deep Learning

Published: 18 March 2026| Version 1 | DOI: 10.17632/6szg7mcbw2.1

Contributors:

Hongguo Jia, Cuilan Zhang, Yuchen Liu

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The document contains the core code for a spatio-temporal dual-driver prediction model, along with five benchmark models for comparison and validation: ARIMA, GRU, LSTM, XGboost, and Random Forest.

Prediction of Urban Surface Deformation by Integrating Time-Series InSAR and Spatiotemporal Dual-Driven Deep Learning

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