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Machine learning application in water quality using satellite data

Monitoring water quality is a critical aspect of environmental sustainability. Poor water quality has an impact not just on aquatic life but also on the ecosystem. The purpose of this systematic review is to identify peer-reviewed literature on the effectiveness of applying machine learning (ML) met...

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Main Authors: Hassan, N., Woo, Chaw Seng
Format: Conference or Workshop Item
Published: 2021
Subjects:
GC Oceanography
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/35738/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115033344&doi=10.1088%2f1755-1315%2f842%2f1%2f012018&partnerID=40&md5=15dfc5b53ddbe478a1ccac02b1a4833e
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spelling my.um.eprints.357382023-11-09T09:25:14Z http://eprints.um.edu.my/35738/ Machine learning application in water quality using satellite data Hassan, N. Woo, Chaw Seng GC Oceanography QA75 Electronic computers. Computer science Monitoring water quality is a critical aspect of environmental sustainability. Poor water quality has an impact not just on aquatic life but also on the ecosystem. The purpose of this systematic review is to identify peer-reviewed literature on the effectiveness of applying machine learning (ML) methodologies to estimate water quality parameters with satellite data. The data was gathered using the Scopus, Web of Science, and IEEE citation databases. Related articles were extracted, selected, and evaluated using advanced keyword search and the PRISMA approach. The bibliographic information from publications written in journals during the previous two decades were collected. Publications that applied ML to water quality parameter retrieval with a focus on the application of satellite data were identified for further systematic review. A search query of 1796 papers identified 113 eligible studies. Popular ML models application were artificial neural network (ANN), random forest (RF), support vector machines (SVM), regression, cubist, genetic programming (GP) and decision tree (DT). Common water quality parameters extracted were chlorophyll-a (Chl-a), temperature, salinity, colored dissolved organic matter (CDOM), suspended solids and turbidity. According to the systematic analysis, ML can be successfully extended to water quality monitoring, allowing researchers to forecast and learn from natural processes in the environment, as well as assess human impacts on an ecosystem. These efforts will also help with restoration programs to ensure that environmental policy guidelines are followed. © Published under licence by IOP Publishing Ltd. 2021-09 Conference or Workshop Item PeerReviewed Hassan, N. and Woo, Chaw Seng (2021) Machine learning application in water quality using satellite data. In: 3rd International Conference on Tropical Resources and Sustainable Sciences, CTReSS 2021, 14 - 15 July 2021, Kelantan, Virtual. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115033344&doi=10.1088%2f1755-1315%2f842%2f1%2f012018&partnerID=40&md5=15dfc5b53ddbe478a1ccac02b1a4833e
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic GC Oceanography
QA75 Electronic computers. Computer science
spellingShingle GC Oceanography
QA75 Electronic computers. Computer science
Hassan, N.
Woo, Chaw Seng
Machine learning application in water quality using satellite data
description Monitoring water quality is a critical aspect of environmental sustainability. Poor water quality has an impact not just on aquatic life but also on the ecosystem. The purpose of this systematic review is to identify peer-reviewed literature on the effectiveness of applying machine learning (ML) methodologies to estimate water quality parameters with satellite data. The data was gathered using the Scopus, Web of Science, and IEEE citation databases. Related articles were extracted, selected, and evaluated using advanced keyword search and the PRISMA approach. The bibliographic information from publications written in journals during the previous two decades were collected. Publications that applied ML to water quality parameter retrieval with a focus on the application of satellite data were identified for further systematic review. A search query of 1796 papers identified 113 eligible studies. Popular ML models application were artificial neural network (ANN), random forest (RF), support vector machines (SVM), regression, cubist, genetic programming (GP) and decision tree (DT). Common water quality parameters extracted were chlorophyll-a (Chl-a), temperature, salinity, colored dissolved organic matter (CDOM), suspended solids and turbidity. According to the systematic analysis, ML can be successfully extended to water quality monitoring, allowing researchers to forecast and learn from natural processes in the environment, as well as assess human impacts on an ecosystem. These efforts will also help with restoration programs to ensure that environmental policy guidelines are followed. © Published under licence by IOP Publishing Ltd.
format Conference or Workshop Item
author Hassan, N.
Woo, Chaw Seng
author_facet Hassan, N.
Woo, Chaw Seng
author_sort Hassan, N.
title Machine learning application in water quality using satellite data
title_short Machine learning application in water quality using satellite data
title_full Machine learning application in water quality using satellite data
title_fullStr Machine learning application in water quality using satellite data
title_full_unstemmed Machine learning application in water quality using satellite data
title_sort machine learning application in water quality using satellite data
publishDate 2021
url http://eprints.um.edu.my/35738/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115033344&doi=10.1088%2f1755-1315%2f842%2f1%2f012018&partnerID=40&md5=15dfc5b53ddbe478a1ccac02b1a4833e
_version_ 1783876631040884736
score 13.211869

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