Environmental Controls on Vertical Land Motion: A Machine Learning Case Study
DOI:
https://doi.org/10.55779/ng63711Keywords:
equivalent water thickness, factor analysis, machine learning, monitoring, random forest, ridge regression, vertical land motionAbstract
This study integrates Ridge Regression (RR) and Random Forest (RF) machine learning (ML) models with geodetic and climatic datasets to investigate vertical land motion (VLM) patterns across the Ifewara-Zungeru fault zone. Temporal variations in equivalent water thickness (ΔEWT), rainfall, precipitation, Earth surface skin temperature (EST), temperature (T2M), and relative humidity at 2 m (RH2M) data from 2002 to 2025 were used to predict VLM. The models were trained using data from 2002 to 2022 while independent evaluation was conducted for the period of 2023-2025 using VLM derived from Small Baseline Subset Interferometric Synthetic Aperture Radar. Both RF and RR models demonstrated good predictive performance, with the RF model slightly outperforming the RR model. The RF model achieved an average Root Mean Squared Error (RMSE) of 0.002 m, and a Symmetric Mean Absolute Percentage Error (SMAPE) of 4.8%, whereas the RR model achieved RMSE of 0.003m, and a SMAPE of 4.8%. Both RR and RF models have very strong correlation coefficient except at the uplift region. Feature importance analysis revealed that ΔEWT was the dominant driver of VLM, contributing over 90% in the RF and 80% with RR model within subsiding regions. In uplift regions, ΔEWT contributed 33% and 11% in the RF and RR models respectively, while T2M emerged as the most influential predictor in the RR model, followed by RH2M. The results highlight the role of hydrological processes in controlling VLM and demonstrate the potential of ML for VLM prediction and highlight the need for continuous monitoring to mitigate hazards.
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