Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City
Source abstract
Abstract
Land subsidence (LS) is a geological hazard driven by both natural conditions and human activities. Traditional LS time-series prediction models often struggle to accurately capture nonlinear data characteristics, leading to suboptimal predictions. To address this issue, this paper introduces a multi-component temporal convolutional gate recurrent unit (MC-TCGRU) model, which integrates a fully adaptive noise-ensemble empirical-mode decomposition algorithm with a deep neural network to account for the complexity of time-series data. The model was validated using typical InSAR subsidence data from Kunming, analyzing the impact of each component on the prediction performance. A comparative analysis with the TCGRU model and models based on seasonal-trend decomposition using LOESS (STL) and empirical-mode decomposition (EMD) revealed that the MC-TCGRU model significantly enhanced the prediction accuracy by reducing the complexity of the original data. The model achieved R² values of 0.90, 0.93, 0.51, 0.93, and 0.96 across five points, outperforming the compared models.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Synthetic Aperture Radar (SAR) Applications and Techniques · Landslides and related hazards · Cryospheric studies and observations
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Hebei Cangzhou Groundwater and Land Subsidence National Observation and Research Station, Cangzhou 061000, China
School of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Funding
the Open Fund of Hebei Cangzhou Groundwater and Land Subsidence National Observation and Research Station
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