Masters Theses

Keywords and Phrases

global groundwater; groundwater monitoring; InSAR; land subsidence; machine learning; remote sensing


"Quantifying groundwater storage loss is becoming increasingly essential globally due limited availability of this major hydrologic component and its long recharge time. Groundwater overdraft gives rises to multiple adverse impacts including land subsidence and permanent groundwater storage loss. In absence of spatially dense monitoring network, publicly available in-situ data, and uniform monitoring strategies, it is challenging to assess the sustained losses from overexploitation of this resource. Remote sensing based techniques have the capacity to fill this gap to increase our groundwater monitoring capacities. Exploring the interrelation between groundwater pumping and land subsidence using remote sensing datasets can be a very effective technique to measure depletion of aquifers. In this study, we developed a machine learning model to explore this relationship with the help of gridded remotely sensed and model-based dataset, and Interferometric Synthetic Aperture Radar (InSAR) based land deformation data. InSAR generated land subsidence data from 36 different regions of the world were used to train a random forests model to map land subsidence globally at a high spatial resolution of ~2 km. The model predicted land subsidence magnitude in three classes: /year, 1-5 cm/year and >5 cm/year. The model found realistic relationship between the driver variables, groundwater pumping and land subsidence with an overall score of 0.84 on the test set. Resulting maps from this model will be incredibly helpful in knowing the true spatial extents of subsidence in known subsiding areas and in locating unknown groundwater stressed regions where subsidence has not been documented before"--Abstract, p. iii


Smith, Ryan G.

Committee Member(s)

Grote, Katherine R.
Maurer, Jeremy


Geosciences and Geological and Petroleum Engineering

Degree Name

M.S. in Geological Engineering


Missouri University of Science and Technology

Publication Date

Summer 2022


viii, 66 pages

Note about bibliography

Includes_bibliographical_references_(pages 55-65)


© 2022 Md Fahim Hasan, All Rights Reserved

Document Type

Thesis - Open Access

File Type




Thesis Number

T 12309