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Land subsidence modelling using tree-based machine learning algorithms.

Omid Rahmati1, Fatemeh Falah2, Seyed Amir Naghibi3

  • 1Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh City, Viet Nam; Faculty of Environment and Labour Safety, Ton Duc Thang University, Ho Chi Minh City, Viet Nam.

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Summary
This summary is machine-generated.

This study applied machine learning to predict land subsidence (LS) risk, finding the Random Forest model most accurate. Groundwater drawdown was the primary driver of LS in the Hamadan plain.

Keywords:
Artificial intelligenceEnvironmental managementGISHazardSpatial analysis

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Area of Science:

  • Environmental Science
  • Geosciences
  • Data Science

Background:

  • Land subsidence (LS) poses significant threats to agriculture and urban infrastructure.
  • Current prediction methods often rely on basic regression or complex hydraulic models.
  • Machine learning approaches for LS risk factor identification and prediction are under-explored.

Purpose of the Study:

  • To compare four tree-based machine learning models for land subsidence hazard modeling.
  • To analyze the influence of topographic, geomorphic, hydrologic, and lithologic factors on LS.
  • To develop a consolidated LS hazard map for the Hamadan plain, Iran.

Main Methods:

  • Utilized Rule-Based Decision Tree (RBDT), Boosted Regression Trees (BRT), Classification And Regression Tree (CART), and Random Forest (RF) algorithms.
  • Prepared thematic layers for elevation, slope, distance from stream, drainage density, groundwater drawdown, and lithology.
  • Evaluated model accuracy using Area Under the Receiver Operating Characteristic Curve (AUC) and True Skill Statistics (TSS).

Main Results:

  • The Random Forest (RF) model demonstrated the lowest predictive error (AUC 96.7% training, 93.8% validation; TSS 0.912 training, 0.904 validation).
  • Boosted Regression Trees (BRT) also showed strong performance.
  • Groundwater drawdown was identified as the most significant factor influencing land subsidence, followed by lithology and distance from streams.

Conclusions:

  • Tree-based machine learning models, particularly RF, are effective for land subsidence hazard mapping.
  • Groundwater drawdown is a critical factor driving land subsidence in the study area.
  • The findings provide valuable insights for land use planning and sustainable groundwater management.