Spatial Heterogeneity Analysis and Machine Learning-Based Forecasting of Land Subsidence in Ho Chi Minh City: A GWR and ConvLSTM Integrated Approach
DOI:
https://doi.org/10.5281/zenodo.18477050Keywords:
Land subsidence, InSAR, Geographically Weighted Regression (GWR),, Machine Learning, Ho Chi Minh City, Ground deformation, Urban planningAbstract
This study develops a framework for simulating and forecasting land subsidence in Ho Chi Minh City, specifically focusing on District 12. By utilizing InSAR time-series subsidence data from 2015-2020 alongside influencing factors such as building density, distance to water bodies, and land use types, the research employs Geographically Weighted Regression (GWR) to analyze the underlying subsidence mechanisms. Experimental results demonstrate significant spatial heterogeneity in land deformation, where Land Use and Distance to Water emerge as the most dominant factors, with average regression coefficients of -0.390 and -0.344, respectively. Furthermore, the study proposes an integrated forecasting system architecture leveraging advanced Machine Learning models, including Random Forest, XGBoost, and ConvLSTM deep learning architectures to predict future surface deformation. Risk zonation results derived from K-means clustering provide effective visual tools for urban planning and early warning systems for geological hazards.
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