Work

Land Subsidence Predictions Based on a Multi-Component Temporal Convolutional Gated Recurrent Unit Model in Kunming City

Year: 2024

Type: article

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: Applied Sciences

DOI: 10.3390/app142110021

Authors: Tao Chen, Di Ning, Yuhang Liu

Institution: China University of Geosciences

Language: English

Topic: Synthetic Aperture Radar (SAR) Applications and Techniques

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,728