Near real-time subsidence monitoring and AI forecasting with multi-depth extensometers
Year: 2025
Type: article
Abstract: Land subsidence induced by excessive groundwater withdrawal poses a growing threat to infrastructure, water resources, and environmental sustainability. Developing robust early warning systems and accurate subsidence forecasts remains a significant challenge. This study integrates high-frequency, real-time deformation data from automated deep extensometers with artificial intelligence (AI) to improve short-term forecasting of land subsidence. The AI-driven model achieves a 35% improvement in predictive accuracy and captures both long-term trends and seasonal variability. We compare extensometer observations with multilayer compaction well (MLCW) data to evaluate their respective advantages. MLCWs provide millimeter-resolution compaction measurements at up to 20 discrete depths, revealing depth-dependent aquifer-system deformation. Extensometers, by contrast, offer continuous measurements at selected depths (10-minute intervals), enabling near-real-time detection of vertical displacement. When combined, the two systems form a hybrid monitoring framework with enhanced spatial and temporal resolution. This integrated approach supports more accurate subsidence assessment and forecasting, informing groundwater management strategies and infrastructure risk mitigation. Our results demonstrate that coupled monitoring and AI modeling are essential tools for sustainable groundwater development in subsidence-prone regions.
Source: Engineering Geology
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
Estimated APC paid: $4,210
OpenAlex ID: W4415939349