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Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan

Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in th...

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Main Authors: Ziyad Sami, Balahaha Fadi, Latif, Sarmad Dashti, Ahmed, Ali Najah, Chow, Ming Fai, Murti, Muhammad Ary, Suhendi, Asep, Ziyad Sami, Balahaha Hadi, Wong, Jee Khai, Birima, Ahmed H., El-Shafie, Ahmed
Format: Article
Published: Nature Research 2022
Subjects:
TA Engineering (General). Civil engineering (General)
Online Access:http://eprints.um.edu.my/42995/
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spelling my.um.eprints.429952023-10-05T04:11:21Z http://eprints.um.edu.my/42995/ Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan Ziyad Sami, Balahaha Fadi Latif, Sarmad Dashti Ahmed, Ali Najah Chow, Ming Fai Murti, Muhammad Ary Suhendi, Asep Ziyad Sami, Balahaha Hadi Wong, Jee Khai Birima, Ahmed H. El-Shafie, Ahmed TA Engineering (General). Civil engineering (General) Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs. Nature Research 2022-03 Article PeerReviewed Ziyad Sami, Balahaha Fadi and Latif, Sarmad Dashti and Ahmed, Ali Najah and Chow, Ming Fai and Murti, Muhammad Ary and Suhendi, Asep and Ziyad Sami, Balahaha Hadi and Wong, Jee Khai and Birima, Ahmed H. and El-Shafie, Ahmed (2022) Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan. Scientific Reports, 12 (1). ISSN 2045-2322, DOI https://doi.org/10.1038/s41598-022-06969-z <https://doi.org/10.1038/s41598-022-06969-z>. 10.1038/s41598-022-06969-z
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 TA Engineering (General). Civil engineering (General)
spellingShingle TA Engineering (General). Civil engineering (General)
Ziyad Sami, Balahaha Fadi
Latif, Sarmad Dashti
Ahmed, Ali Najah
Chow, Ming Fai
Murti, Muhammad Ary
Suhendi, Asep
Ziyad Sami, Balahaha Hadi
Wong, Jee Khai
Birima, Ahmed H.
El-Shafie, Ahmed
Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
description Water quality status in terms of one crucial parameter such as dissolved oxygen (D.O.) has been an important concern in the Fei-Tsui reservoir for decades since it's the primary water source for Taipei City. Therefore, this study aims to develop a reliable prediction model to predict D.O. in the Fei-Tsui reservoir for better water quality monitoring. The proposed model is an artificial neural network (ANN) with one hidden layer. Twenty-nine years of water quality data have been used to validate the accuracy of the proposed model. A different number of neurons have been investigated to optimize the model's accuracy. Statistical indices have been used to examine the reliability of the model. In addition to that, sensitivity analysis has been carried out to investigate the model's sensitivity to the input parameters. The results revealed the proposed model capable of capturing the dissolved oxygen's nonlinearity with an acceptable level of accuracy where the R-squared value was equal to 0.98. The optimum number of neurons was found to be equal to 15-neuron. Sensitivity analysis shows that the model can predict D.O. where four input parameters have been included as input where the d-factor value was equal to 0.010. This main achievement and finding will significantly impact the water quality status in reservoirs. Having such a simple and accurate model embedded in IoT devices to monitor and predict water quality parameters in real-time would ease the decision-makers and managers to control the pollution risk and support their decisions to improve water quality in reservoirs.
format Article
author Ziyad Sami, Balahaha Fadi
Latif, Sarmad Dashti
Ahmed, Ali Najah
Chow, Ming Fai
Murti, Muhammad Ary
Suhendi, Asep
Ziyad Sami, Balahaha Hadi
Wong, Jee Khai
Birima, Ahmed H.
El-Shafie, Ahmed
author_facet Ziyad Sami, Balahaha Fadi
Latif, Sarmad Dashti
Ahmed, Ali Najah
Chow, Ming Fai
Murti, Muhammad Ary
Suhendi, Asep
Ziyad Sami, Balahaha Hadi
Wong, Jee Khai
Birima, Ahmed H.
El-Shafie, Ahmed
author_sort Ziyad Sami, Balahaha Fadi
title Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
title_short Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
title_full Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
title_fullStr Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
title_full_unstemmed Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan
title_sort machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of feitsui reservoir, taiwan
publisher Nature Research
publishDate 2022
url http://eprints.um.edu.my/42995/
_version_ 1781704663189422080
score 13.211869

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