Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar

Water is an essential element on this earth in which humans, animals, and plants depend entirely on water sources for a living. Unfortunately, human activities have polluted water sources. Water pollution is typically caused by organic or chemical impurities. Organic matter is contaminated by microo...

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Main Author:	Jaffar, Azhar
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Central nervous system
Online Access:	https://ir.uitm.edu.my/id/eprint/106817/1/106817.pdf
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spelling	my-uitm-ir.1068172024-12-04T14:52:49Z Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar 2023 Jaffar, Azhar Central nervous system Qualities of water. Water quality Water is an essential element on this earth in which humans, animals, and plants depend entirely on water sources for a living. Unfortunately, human activities have polluted water sources. Water pollution is typically caused by organic or chemical impurities. Organic matter is contaminated by microorganisms such as bacteria and viruses that are formed by human, animal, and plant waste. Pesticides such as nitrates and phosphates, industrial acids, hydrocarbons, home products, and heavy metals all contribute to chemical contamination. Water is often examined for pollution in its physical, biological, and chemical properties. In Malaysia, the Klang River experiences high water pollution levels every year and takes a long time to rectify the problem. As a solution, each river must have a smart system capable of assessing water quality and monitoring the water quality index readings. In redressing this situation, water quality must be tested for pollution. However, to test and determine the reasonable acceptance level for all water parameters takes some time. Some of the value of the parameter is not available impromptu. The most critical indicator parameter in the assessment of water quality is the Biochemical Oxygen Demand (BOD). The amount of oxygen required to eliminate waste organic matter from water is measured as BOD. When assessing water quality, the most important factor is BOD. BOD readings can be obtained through time-consuming laboratory tests that are influenced by environmental factors at the time the test is performed. Therefore, the emphasis is on implementing environmentally friendly techniques that do not necessitate the use of physical tools for measurement. The deep learning neural network has emerged over the past several years as a promising and versatile method for data prediction in many fields. A deep learning neural network is used by making predictions using samples and previous readings of the water parameters being tested. Water quality data collected over a seven-year period was utilized to validate the deep learning neural network model's accuracy. Nevertheless, a problem arises where the water quality data recorded throughout the period is not uniform due to various human and environmental factors. This suggests that the data gathered by the personnel does not adhere to a set timetable and that there may be variations in the water's quality because of heat, rainfall, and turbidity. The uneven gap found in the water quality data is restructured by using interpolation techniques to reorganize the data. Interpolation is a form of mathematics for predicting the values between known data points. This procedure aids in filling in the gaps. To compare the prediction results made by deep learning neural network techniques, three models were used, namely LSTM, bi-LSTM and GRU. 2023 Thesis https://ir.uitm.edu.my/id/eprint/106817/ https://ir.uitm.edu.my/id/eprint/106817/1/106817.pdf text en public phd doctoral Universiti Teknologi MARA (UiTM) College of Engineering M. Thamrin, Norashikin
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	M. Thamrin, Norashikin
topic	Central nervous system Central nervous system
spellingShingle	Central nervous system Central nervous system Jaffar, Azhar Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
description	Water is an essential element on this earth in which humans, animals, and plants depend entirely on water sources for a living. Unfortunately, human activities have polluted water sources. Water pollution is typically caused by organic or chemical impurities. Organic matter is contaminated by microorganisms such as bacteria and viruses that are formed by human, animal, and plant waste. Pesticides such as nitrates and phosphates, industrial acids, hydrocarbons, home products, and heavy metals all contribute to chemical contamination. Water is often examined for pollution in its physical, biological, and chemical properties. In Malaysia, the Klang River experiences high water pollution levels every year and takes a long time to rectify the problem. As a solution, each river must have a smart system capable of assessing water quality and monitoring the water quality index readings. In redressing this situation, water quality must be tested for pollution. However, to test and determine the reasonable acceptance level for all water parameters takes some time. Some of the value of the parameter is not available impromptu. The most critical indicator parameter in the assessment of water quality is the Biochemical Oxygen Demand (BOD). The amount of oxygen required to eliminate waste organic matter from water is measured as BOD. When assessing water quality, the most important factor is BOD. BOD readings can be obtained through time-consuming laboratory tests that are influenced by environmental factors at the time the test is performed. Therefore, the emphasis is on implementing environmentally friendly techniques that do not necessitate the use of physical tools for measurement. The deep learning neural network has emerged over the past several years as a promising and versatile method for data prediction in many fields. A deep learning neural network is used by making predictions using samples and previous readings of the water parameters being tested. Water quality data collected over a seven-year period was utilized to validate the deep learning neural network model's accuracy. Nevertheless, a problem arises where the water quality data recorded throughout the period is not uniform due to various human and environmental factors. This suggests that the data gathered by the personnel does not adhere to a set timetable and that there may be variations in the water's quality because of heat, rainfall, and turbidity. The uneven gap found in the water quality data is restructured by using interpolation techniques to reorganize the data. Interpolation is a form of mathematics for predicting the values between known data points. This procedure aids in filling in the gaps. To compare the prediction results made by deep learning neural network techniques, three models were used, namely LSTM, bi-LSTM and GRU.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Jaffar, Azhar
author_facet	Jaffar, Azhar
author_sort	Jaffar, Azhar
title	Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
title_short	Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
title_full	Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
title_fullStr	Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
title_full_unstemmed	Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar
title_sort	biochemical oxygen demand (bod) predictive model based on water quality characteristics with deep learning neural network / azhar jaffar
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	College of Engineering
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/106817/1/106817.pdf
_version_	1818588188265938944

Biochemical oxygen demand (BOD) predictive model based on water quality characteristics with deep learning neural network / Azhar Jaffar

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