Cover Image

Prediction of water quality for lake monitoring system using machine learning approach

Water quality in lakes is a critical issue due to its direct influence on public health, biological integrity of natural resources, and the economy. There are a variety of lakes, from small to big, natural and manmade reservoirs to natural lakes. Even though the lakes only consist small part of wate...

Full description

Saved in:
Bibliographic Details
Main Author: Hong, Yuen Mun
Format: Academic Exercise
Language:English
English
Published: 2022
Subjects:
Q1-390 Science (General)
QH1-278.5 Natural history (General)
Online Access:https://eprints.ums.edu.my/id/eprint/33294/1/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.24pages.pdf
https://eprints.ums.edu.my/id/eprint/33294/2/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.pdf
https://eprints.ums.edu.my/id/eprint/33294/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ums.eprints.33294
record_format eprints
spelling my.ums.eprints.332942022-07-18T11:22:44Z https://eprints.ums.edu.my/id/eprint/33294/ Prediction of water quality for lake monitoring system using machine learning approach Hong, Yuen Mun Q1-390 Science (General) QH1-278.5 Natural history (General) Water quality in lakes is a critical issue due to its direct influence on public health, biological integrity of natural resources, and the economy. There are a variety of lakes, from small to big, natural and manmade reservoirs to natural lakes. Even though the lakes only consist small part of water on our planet, they play an important role in earth’s biosphere, climate changes, land-use changes, and anthropogenic changes due to various urban and industrial development can lead to hydrological, chemical, and biological changes in watersheds and freshwater ecosystems resulting in altered water quality. The deteriorating quality of natural water resources like lakes is one of the dire problems and most concerning issues faced by humanity. Lack of quality lake water is most likely because lake water became contaminated due to various factors such as humans, industrial, commercial activities, and natural processes. To understand the impact of changes from upstream or surrounding watersheds and within a lake on water quality is important to people who live nearby or visit the lake and is also fundamental in providing better ecological and environmental strategies and mitigation methods to protect the freshwater ecosystems. Dissolved oxygen and other water quality will affect the growth, reproduction, and survivability of freshwater organisms. Climate variations can directly affect the temperature of an aquatic system through the surface heat exchange between the water and the surrounding atmosphere and further influence water quality characteristics. Monitoring and modeling approaches have been used by volunteers, biologists, water resources managers, engineers, and scientists to understand and further study water quality issues in the lake. Therefore, the monitoring and prediction of lake water quality will provide more in-depth and necessary information and evidence to help in managing the water quality. Monitoring data are necessary for model calibration and validation before the model can be used for scenario study, sensitive analysis, and future projection under certain changes in lakes water. In this project, a machine learning approach is proposed to assist the prediction of the lake water quality. The evaluation will then be used to predict the quality of water. 2022 Academic Exercise NonPeerReviewed text en https://eprints.ums.edu.my/id/eprint/33294/1/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.24pages.pdf text en https://eprints.ums.edu.my/id/eprint/33294/2/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.pdf Hong, Yuen Mun (2022) Prediction of water quality for lake monitoring system using machine learning approach. University Malaysia Sabah. (Unpublished)
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic Q1-390 Science (General)
QH1-278.5 Natural history (General)
spellingShingle Q1-390 Science (General)
QH1-278.5 Natural history (General)
Hong, Yuen Mun
Prediction of water quality for lake monitoring system using machine learning approach
description Water quality in lakes is a critical issue due to its direct influence on public health, biological integrity of natural resources, and the economy. There are a variety of lakes, from small to big, natural and manmade reservoirs to natural lakes. Even though the lakes only consist small part of water on our planet, they play an important role in earth’s biosphere, climate changes, land-use changes, and anthropogenic changes due to various urban and industrial development can lead to hydrological, chemical, and biological changes in watersheds and freshwater ecosystems resulting in altered water quality. The deteriorating quality of natural water resources like lakes is one of the dire problems and most concerning issues faced by humanity. Lack of quality lake water is most likely because lake water became contaminated due to various factors such as humans, industrial, commercial activities, and natural processes. To understand the impact of changes from upstream or surrounding watersheds and within a lake on water quality is important to people who live nearby or visit the lake and is also fundamental in providing better ecological and environmental strategies and mitigation methods to protect the freshwater ecosystems. Dissolved oxygen and other water quality will affect the growth, reproduction, and survivability of freshwater organisms. Climate variations can directly affect the temperature of an aquatic system through the surface heat exchange between the water and the surrounding atmosphere and further influence water quality characteristics. Monitoring and modeling approaches have been used by volunteers, biologists, water resources managers, engineers, and scientists to understand and further study water quality issues in the lake. Therefore, the monitoring and prediction of lake water quality will provide more in-depth and necessary information and evidence to help in managing the water quality. Monitoring data are necessary for model calibration and validation before the model can be used for scenario study, sensitive analysis, and future projection under certain changes in lakes water. In this project, a machine learning approach is proposed to assist the prediction of the lake water quality. The evaluation will then be used to predict the quality of water.
format Academic Exercise
author Hong, Yuen Mun
author_facet Hong, Yuen Mun
author_sort Hong, Yuen Mun
title Prediction of water quality for lake monitoring system using machine learning approach
title_short Prediction of water quality for lake monitoring system using machine learning approach
title_full Prediction of water quality for lake monitoring system using machine learning approach
title_fullStr Prediction of water quality for lake monitoring system using machine learning approach
title_full_unstemmed Prediction of water quality for lake monitoring system using machine learning approach
title_sort prediction of water quality for lake monitoring system using machine learning approach
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/33294/1/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.24pages.pdf
https://eprints.ums.edu.my/id/eprint/33294/2/Prediction%20of%20Water%20Quality%20for%20Lake%20Monitoring%20System%20using%20Machine%20Learning%20Approach.pdf
https://eprints.ums.edu.my/id/eprint/33294/
_version_ 1760231144599584768
score 13.211869

Similar Items