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Current Analytical Chemistry


ISSN (Print): 1573-4110
ISSN (Online): 1875-6727

Review Article

Water Pollution Monitoring through Remote Sensing

Author(s): Krishnamurthy Vallidevi*, Kannappan P. Gopinath , Krishnan K. Nagarajan, D. Gnana Prakash, Gurijala Sudhamsu, S. Sudhish and Salma A. Al-Zahrani

Volume 17, Issue 6, 2021

Published on: 06 February, 2020

Page: [802 - 814] Pages: 13

DOI: 10.2174/1573411016666200206095055

Price: $65


Background: Water is undoubtedly a very precious resource that helps life thrive on this planet, and its pollution is a problem, which is potent in erasing almost all forms of life on the earth. Hence, considering the magnitude of this problem, studies and experiments have been focused (from the day water pollution was recognised as an eminent issue) on monitoring and finding possible solutions for water pollution. While the latter deals with treating and purifying water using a variety of concepts, monitoring deals with the continuous assessment of water quality of a waterbody.

Methodology: There are several methods for monitoring purposes, and remote sensing is a popular choice, thanks to its wide applicability and flexibility in implementation. Remote sensing deals with collecting data about a place (which is to be monitored) and sending the data to another ‘remote’ location for analysis. This article provides a description of some methods employed in recent times for remote sensing and a short section which deals with the analysis of the remotely sensed data using machine learning / deep learning models, hence making the reader aware of the concept of remote sensing and its scope for monitoring water pollution (or any form of pollution) in the future.

Conclusion: The detailed comparative analysis of these methods showed that sensor-based water quality monitoring with Geographical Information System (GIS) would be more efficient for the detection of water pollutants. Further research in this field may introduce many advancements to enable efficient water pollution detection techniques.

Keywords: Deep learning, machine learning, pollution, remote monitoring, sensors, water quality.

Erratum In:
Water Pollution Monitoring through Remote Sensing

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