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Reconstructing missing time-varying land subsidence data using back propagation neural network with principal

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This study reconstructs missing land subsidence data using a novel algorithm considering eight factors. The backpropagation neural network accurately fills data gaps, avoiding complex hydrogeological models.

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Area of Science:

  • Geophysics
  • Geology
  • Data Science

Background:

  • Land subsidence is a significant geophysical hazard requiring detailed temporal data.
  • Understanding regional subsidence patterns is crucial for effective management.
  • The Choshui Delta, Taiwan, faces challenges with missing time-varying subsidence data.

Purpose of the Study:

  • To develop and validate a novel algorithm for reconstructing missing time-varying land subsidence data.
  • To accurately capture the interplay of multiple factors influencing land subsidence.
  • To provide a data-driven approach that bypasses the need for complex numerical models.

Main Methods:

  • A multi-factorial approach considering eight influential factors.
  • Principal Component Analysis (PCA) to identify significant factors and components.
  • Backpropagation neural network (BPNN) for reconstructing time-dependent subsidence data.

Main Results:

  • The proposed model accurately reconstructs missing land subsidence data.
  • Validation metrics (RMSE, R-squared) confirm high accuracy.
  • The method effectively integrates PCA-derived components for data reconstruction.

Conclusions:

  • The developed neural network model offers a highly accurate solution for reconstructing land subsidence data.
  • This approach simplifies the analysis by eliminating the need for complex hydrogeological simulations.
  • The findings are applicable to similar subsidence monitoring and data reconstruction challenges.