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...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Institute of Electrical and Electronics Engineers Inc.
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39082/ 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 |
| _version_ | 1848825678522220544 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T03:32:45Z |
| format | Conference or Workshop Item |
| id | ump-39082 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:32:45Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers Inc. |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-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 (Published) https://doi.org/10.1109/RFM56185.2022.10065258 |
| 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 |
| title | 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_short | Biofloc farming with IoT and machine learning predictive water quality system |
| title_sort | biofloc farming with iot and machine learning predictive water quality system |
| 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 |
| url | http://umpir.ump.edu.my/id/eprint/39082/ http://umpir.ump.edu.my/id/eprint/39082/ 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 |