Employing machine learning to document trends and seasonality of groundwater-induced subsidence

Sumriti Ranjan Patra, Hone-Jay Chu () and Tatas
Additional contact information
Sumriti Ranjan Patra: National Cheng Kung University
Hone-Jay Chu: National Cheng Kung University
Tatas: Institut Teknologi Sepuluh Nopember, Manyar Campus

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 4, No 50, 5007-5031

Abstract: Abstract Land subsidence is largely influenced by the declining trend and seasonal fluctuations of groundwater levels. This study proposes a comprehensive data-driven subsidence trend identification and forecasting system based on hydraulic head observations by integrating seasonality and trend decomposition with machine learning. The study firstly identifies the decomposition components of the deformation and hydraulic head time series data, and formulates a relationship between subsidence trend and hydraulic head to detect regions suffering from significant subsidence. Overall deformation was estimated using head changes firstly, and then subsidence trend was identified after removal of the non-trend component. Results revealed that the deformation varied with seasonal groundwater fluctuations that were dominated by a strong response during wet and dry seasons. Seasonality between subsidence and groundwater change is strongly correlated. The subsidence trend can be determined from the removal of the seasonality related to head change, especially near the proximal and mid-fan areas. A subsidence hotspot with a significantly steeper trend (> 4 cm/year) was observed in inland areas (mid-fan). The R2 achieved a strong fit when model estimation was compared with the observed deformation trend for the testing period (2019–20). The subsidence forecasting system developed in this study could enhance subsidence and groundwater management to offer the potential of identifying areas experiencing increased irrecoverable subsidence by analyzing the estimated subsidence trend associated with groundwater changes.

Keywords: Groundwater; Land subsidence forecasting; Seasonality and trend decomposition; Machine learning (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11069-024-06991-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:nathaz:v:121:y:2025:i:4:d:10.1007_s11069-024-06991-6

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11069

DOI: 10.1007/s11069-024-06991-6

Access Statistics for this article

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards is currently edited by Thomas Glade, Tad S. Murty and Vladimír Schenk

More articles in Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards from Springer, International Society for the Prevention and Mitigation of Natural Hazards
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().