Geospatial Machine Learning Approaches for Studying Urbanization Impact, Surface Reflectance Patterns, and Groundwater Health Risks

Author

Bibhash Nath, CUNY Hunter CollegeFollow

Date of Award

Fall 1-3-2025

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Geography

First Advisor

Wenge Ni-Meister

Second Advisor

Shipeng Sun

Academic Program Adviser

Shipeng Sun

Abstract

Geospatial machine learning techniques have been used to study: the impact of urbanization on land use and land cover change, surface reflectance patterns in boreal regions, and groundwater health risks from arsenic in India. These studies combined spatial data, remote sensing, and predictive models to gain valuable insights for sustainable living and protecting human health.

Comments

Chapter 1:

This chapter was originally published in Groundwater for Sustainable Development, available at https://doi.org/10.1016/j.gsd.2020.100500 . Copyright 2020 Elsevier B.V.

This chapter is reprinted [in full] with permission from Elsevier.

“As an Elsevier journal author, you retain the right to Include the article in a thesis or dissertation (provided that this is not to be published commercially) whether in full or in part, subject to proper acknowledgment; see https://www.elsevier.com/about/policies/copyright for more information. As this is a retained right, no written permission from Elsevier is necessary. As outlined in our permissions licenses, this extends to the posting to your university’s digital repository of the thesis provided that if you include the published journal article (PJA) version, it is embedded in your thesis only and not separately downloadable.”

Chapter 2:

This chapter was originally published in Remote Sensing, available at https://doi.org/10.3390/rs13163108

Copyright 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. (https://creativecommons.org/licenses/by/4.0/).

Chapter 3:

This chapter was originally published in ACS EST Water, available at

https://doi.org/10.1021/acsestwater.2c00263 . Copyright 2022 American Chemical Society

This chapter is reprinted “in full” with permission from American Chemical Society.

https://pubs.acs.org/pb-assets/acspubs/Migrated/dissertation-1632927826810.pdf

“Reuse/Republication of the Entire Work in Theses”

Appropriate credit should read: "Reprinted with permission from {COMPLETE REFERENCE CITATION}. Copyright {YEAR} American Chemical Society." Insert appropriate information in place of the capitalized words.

Recommended Citation

Nath, Bibhash, "Geospatial Machine Learning Approaches for Studying Urbanization Impact, Surface Reflectance Patterns, and Groundwater Health Risks" (2025). CUNY Academic Works.
https://academicworks.cuny.edu/hc_sas_etds/1250