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Related Experiment Video

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Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and

Bahram Choubin1, Kourosh Shirani2, Farzaneh Sajedi Hosseini3

  • 1Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran.

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Summary

This study integrates radar data and machine learning to simulate land subsidence rates, identifying key drivers like precipitation and groundwater withdrawal. Ensemble models, particularly a nonlinear approach, demonstrated superior performance in predicting subsidence.

Keywords:
DInSAREnsembleFeature eliminationLand subsidenceMachine learningRadar interferometry

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Area of Science:

  • Geosciences and Remote Sensing
  • Environmental Science
  • Water Resource Management

Background:

  • Land subsidence poses a significant challenge for land and water resource management.
  • Radar datasets offer a cost-effective method for monitoring subsidence.
  • Identifying key drivers is crucial for accurate subsidence modeling.

Purpose of the Study:

  • To develop an integrated radar-and-ensemble-based method for simulating land subsidence rates.
  • To identify the primary drivers influencing land subsidence using machine learning.
  • To compare the performance of individual and ensemble machine learning models for subsidence prediction.

Main Methods:

  • Utilized 52 pairs of radar images from 2014-2019 to detect subsidence.
  • Employed the simulated annealing (SA) algorithm to identify key subsidence drivers.
  • Compared three individual machine learning models (SVM, GP, BART) and three ensemble approaches for subsidence modeling.

Main Results:

  • Subsidence rates varied from 0 to 59 cm between 2014 and 2019.
  • Radar-derived subsidence showed a strong correlation with geodynamic station data (R² = 0.99).
  • Key drivers identified include precipitation, elevation, fine-grained materials, groundwater withdrawal, distance to roads, groundwater decline, and aquifer thickness.
  • Ensemble models outperformed individual models, with the nonlinear ensemble (BART combination) showing the best performance (RMSE = 0.061, R² = 0.83).

Conclusions:

  • The nonlinear ensemble approach effectively simulates land subsidence rates.
  • Fine-grained materials in aquifer systems significantly influence subsidence response to groundwater changes.
  • The interaction of groundwater decline, low recharge, and fine-grained materials intensifies land subsidence, posing a considerable risk.