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Biofloc farming with IoT and machine learning predictive water quality system

Biofloc fish farming system depends on full-time monitoring of water quality. The Internet of Things (IoT) can play a vital role in promoting development. However, only a few are able to do stream or real-time predictive analytics at a high cost. Therefore, This article introduces a Biofloc monitori...

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Main Authors: Bakhit, Abdelmoneim Ahmed, Mohd Faizal, Jamlos, Alhaj, Nura Abdalrhman, Rizalman, Mamat
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers Inc. 2022
Subjects:
T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TL Motor vehicles. Aeronautics. Astronautics
Online Access:http://umpir.ump.edu.my/id/eprint/39082/1/Biofloc%20farming%20with%20iot%20and%20machine%20learning%20predictive%20water%20quality%20system.pdf
http://umpir.ump.edu.my/id/eprint/39082/2/Biofloc%20farming%20with%20IoT%20and%20machine%20learning%20predictive%20water%20quality%20system_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39082/
https://doi.org/10.1109/RFM56185.2022.10065258
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spelling my.ump.umpir.390822023-11-14T03:43:06Z http://umpir.ump.edu.my/id/eprint/39082/ Biofloc farming with IoT and machine learning predictive water quality system Bakhit, Abdelmoneim Ahmed Mohd Faizal, Jamlos Alhaj, Nura Abdalrhman Rizalman, Mamat T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TL Motor vehicles. Aeronautics. Astronautics Biofloc fish farming system depends on full-time monitoring of water quality. The Internet of Things (IoT) can play a vital role in promoting development. However, only a few are able to do stream or real-time predictive analytics at a high cost. Therefore, This article introduces a Biofloc monitoring system based on IoT., which is proficient in performing stream analytics and predictive at a lower cost. This paper evaluates the predictive analytics of the Autoregressive Integrated Moving Average (ARIMA) based on Percentage Error (PE) and Prediction Accuracy (PA). Findings show that ARIMA's PE is 1.96%, 7.83 %, 1.78%, 12.17%, 4.52% and 0.58%, for DO, EC, pH TDS, Temperature and water volume, respectively which led to achieving higher prediction accuracy (PA) percentage of 98.03%, 92.16%, 98.21%, 87.82%, 95.47% and 99.41% correspondingly. Institute of Electrical and Electronics Engineers Inc. 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39082/1/Biofloc%20farming%20with%20iot%20and%20machine%20learning%20predictive%20water%20quality%20system.pdf pdf en http://umpir.ump.edu.my/id/eprint/39082/2/Biofloc%20farming%20with%20IoT%20and%20machine%20learning%20predictive%20water%20quality%20system_ABS.pdf Bakhit, Abdelmoneim Ahmed and Mohd Faizal, Jamlos and Alhaj, Nura Abdalrhman and Rizalman, Mamat (2022) Biofloc farming with IoT and machine learning predictive water quality system. In: Proceedings - 2022 RFM IEEE International RF and Microwave Conference, RFM 2022, 19-21 December 2022 , Kuala Lumpur. pp. 1-4. (187406). ISBN 978-166548977-5 https://doi.org/10.1109/RFM56185.2022.10065258
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
English
topic T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TL Motor vehicles. Aeronautics. Astronautics
spellingShingle T Technology (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
TL Motor vehicles. Aeronautics. Astronautics
Bakhit, Abdelmoneim Ahmed
Mohd Faizal, Jamlos
Alhaj, Nura Abdalrhman
Rizalman, Mamat
Biofloc farming with IoT and machine learning predictive water quality system
description Biofloc fish farming system depends on full-time monitoring of water quality. The Internet of Things (IoT) can play a vital role in promoting development. However, only a few are able to do stream or real-time predictive analytics at a high cost. Therefore, This article introduces a Biofloc monitoring system based on IoT., which is proficient in performing stream analytics and predictive at a lower cost. This paper evaluates the predictive analytics of the Autoregressive Integrated Moving Average (ARIMA) based on Percentage Error (PE) and Prediction Accuracy (PA). Findings show that ARIMA's PE is 1.96%, 7.83 %, 1.78%, 12.17%, 4.52% and 0.58%, for DO, EC, pH TDS, Temperature and water volume, respectively which led to achieving higher prediction accuracy (PA) percentage of 98.03%, 92.16%, 98.21%, 87.82%, 95.47% and 99.41% correspondingly.
format Conference or Workshop Item
author Bakhit, Abdelmoneim Ahmed
Mohd Faizal, Jamlos
Alhaj, Nura Abdalrhman
Rizalman, Mamat
author_facet Bakhit, Abdelmoneim Ahmed
Mohd Faizal, Jamlos
Alhaj, Nura Abdalrhman
Rizalman, Mamat
author_sort Bakhit, Abdelmoneim Ahmed
title Biofloc farming with IoT and machine learning predictive water quality system
title_short Biofloc farming with IoT and machine learning predictive water quality system
title_full Biofloc farming with IoT and machine learning predictive water quality system
title_fullStr Biofloc farming with IoT and machine learning predictive water quality system
title_full_unstemmed Biofloc farming with IoT and machine learning predictive water quality system
title_sort biofloc farming with iot and machine learning predictive water quality system
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39082/1/Biofloc%20farming%20with%20iot%20and%20machine%20learning%20predictive%20water%20quality%20system.pdf
http://umpir.ump.edu.my/id/eprint/39082/2/Biofloc%20farming%20with%20IoT%20and%20machine%20learning%20predictive%20water%20quality%20system_ABS.pdf
http://umpir.ump.edu.my/id/eprint/39082/
https://doi.org/10.1109/RFM56185.2022.10065258
_version_ 1822923822412595200
score 13.232414

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