Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land
Aliakbar Mohammadifar1, Hamid Gholami2, Shahram Golzari3,4
1Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
Environmental Science and Pollution Research International
|November 12, 2022
Summary
This study introduces a new method combining feature selection, ensemble deep learning (EDL), and active learning (AL) to predict land subsidence (LS) hazard and rate. Aquifer loss is identified as the primary driver of land subsidence.
Area of Science:
- Geosciences
- Environmental Science
- Remote Sensing
Background:
- Land subsidence (LS) poses significant risks to infrastructure and ecosystems.
- Accurate prediction of LS hazard and rate is crucial for effective land-use planning and risk mitigation.
- Existing methods may lack the precision to capture the complex factors influencing LS.
Purpose of the Study:
- To develop and validate a novel methodology for predicting land subsidence (LS) hazard and rate, including uncertainty quantification.
- To identify key controlling features of LS using advanced statistical and machine learning techniques.
- To map LS susceptibility and rate across the Minab and Shamil-Nian plains in southern Iran.
Main Methods:
- Feature selection using ridge regression to identify critical LS controlling factors.
- Ensemble deep learning (EDL) models, including stacked (SEDL) and voting (VEDL) approaches, were constructed using five dense deep learning models.
- Active learning (AL) was integrated with the SEDL model (SEDL-AL) for enhanced LS rate quantification and uncertainty assessment.
Main Results:
- The SEDL model accurately classified LS hazards, with significant portions of the study area falling into moderate, high, and very high hazard categories.
- Aquifer loss (groundwater drawdown) was identified as the most influential factor driving LS.
- The SEDL-AL model demonstrated superior performance (R² > 95%) in quantifying LS rates, ranging from 0 to 48.1 cm, with the highest rates observed in the Minab plain.
Conclusions:
- The developed EDL and AL integrated approach provides a robust and accurate tool for spatial mapping of LS hazard, rate, and associated uncertainty.
- Understanding the impact of groundwater drawdown is critical for managing land subsidence risks.
- The methodology offers valuable insights for sustainable water resource management and urban planning in subsidence-prone regions.
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