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ORIGINAL RESEARCH ARTICLE

Mapping chronic environmental pressure in Goa using machine learning and deep learning

Madhusudhan Das1 ,  Saurabh K. Gupta1 ,  Shruti Kanga2* ,  Divyansh Kumar Pandey2 ,  Bhartendu Sajan1 ,  Gowhar Meraj3,4 ,  Suraj Kumar Singh1
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1 Centre for Climate Change and Water Research, Suresh Gyan Vihar University, Jaipur, Rajasthan , India
2 Department of Geography, School of Environment and Earth Sciences, Central University of Punjab, Bathinda, Punjab , India
3 Center of Geospatial Intelligence, American University of Sharjah, Sharjah , United Arab Emirates
4 Department of Civil Engineering, College of Engineering, American University of Sharjah, Sharjah , United Arab Emirates
AJWEP, 026340243 https://doi.org/10.36922/AJWEP026340243
Received: 22 August 2026 | Revised: 16 October 2026 | Accepted: 16 October 2026 | Published online: 5 November 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Coastal landscapes experience overlapping environmental pressures that require integrated spatial assessment. This study combines satellite and geospatial indicators to map chronic environmental pressure in Goa, India. Fifteen predictors represent atmospheric composition, terrain, land-surface conditions, land use, and human activity. Time-filtered products cover available observations within a nominal 2018–2023 period, alongside static and differently dated products. Of 89 expert-informed reference records, 81 supplied predictor information for three tree-based algorithms and four neural-network architectures. Their probabilities were combined into an environmental-pressure classification. Low-, moderate-, and high-classes accounted for 56.75%, 30.54%, and 12.71% of valid mapped pixels, respectively. Higher scores characterised urban and industrial corridors, whereas forested eastern areas generally recorded lower scores. Night-time lights had the largest contribution to the high-class extreme gradient boosting Shapley additive explanations. Model scores align with the expert-informed reference classes. The integrated maps provide a relative screening framework in selecting locations for further environmental observation.

Graphical abstract
Keywords
Environmental pressure
Spatial screening
Remote sensing
Machine learning
Deep learning
Model interpretation
Goa
Funding
None.
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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