Biofloc farming with IoT and machine learning predictive water quality system

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

[img] Pdf
Biofloc farming with iot and machine learning predictive water quality system.pdf
Restricted to Repository staff only

Download (1MB) | Request a copy
Biofloc farming with IoT and machine learning predictive water quality system_ABS.pdf

Download (270kB) | Preview


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.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Aquaculture monitoring system; ARIMA Forecasting Model; Biofloc technology; Internet of Things
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TK Electrical engineering. Electronics Nuclear engineering
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Faculty/Division: Institute of Postgraduate Studies
College of Engineering
Faculty of Electrical and Electronic Engineering Technology
Faculty of Mechanical and Automotive Engineering Technology
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 14 Nov 2023 03:43
Last Modified: 14 Nov 2023 03:43
Download Statistic: View Download Statistics

Actions (login required)

View Item View Item