Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans

Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans

This study presents a semi-automated approach for assessing water quality in the Sundarbans, a critical and vulnerable ecosystem, using machine learning (ML) models integrated with field and remotely-sensed data. Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS...

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Bibliographic Details
Main Authors: Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque
Format: Article
Language:English
Published: Elsevier 2025-02-01
Series:Heliyon
Subjects:
Surface water quality
Sundarbans
Google earth engine (GEE)
Machine learning (ML)
Chlorophyll-a
Eutrophication
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844025007844
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