International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 10, Issue 7 (July 2023), Pages: 109-126

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 Original Research Paper

Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniques

 Author(s): 

 Haziq Nazmi, Nor Zainah Siau, Arif Bramantoro *, Wida Susanty Suhaili

 Affiliation(s):

 School of Computing and Informatics, Universiti Teknologi Brunei, Bandar Seri Begawan, Brunei Darussalam

  Full Text - PDF          XML

 * Corresponding Author. 

  Corresponding author's ORCID profile: https://orcid.org/0000-0003-2772-9427

 Digital Object Identifier: 

 https://doi.org/10.21833/ijaas.2023.07.013

 Abstract:

The aquaculture industry has witnessed significant global growth, offering opportunities for sustainable fish production. This research delves into the application of data analytics to develop an appropriate predictive model, utilizing diverse machine learning and statistical techniques, to forecast marine fish production within Brunei Darussalam's aquaculture sector. Employing a machine learning-based algorithm, the study aims to achieve enhanced prediction accuracy, thereby providing novel insights into fish production dynamics. The primary objective of this research is to equip the industry with alternative decision-making tools, leveraging predictive modeling, to identify trends and bolster strategic planning in farm activities, ultimately optimizing marine fish aquaculture production in Brunei. The study employs various time series and machine learning techniques to generate a precise predictive model, effectively capturing the inherent seasonal and trend patterns within the time-series data. To construct the model, the research incorporates notable algorithms, including autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), linear regression, random forest, multilayer perceptron (MLP), and Prophet, in conjunction with correlation analysis. Evaluation of the model's performance and selection of the optimal forecasting model are based on mean absolute percentage error (MAPE) and root mean squared error (RMSE) metrics, ensuring a robust analysis of time series data. Notably, this pioneering research stands as the first-ever attempt to forecast marine fish production in Brunei Darussalam, setting a benchmark unmatched by any existing baseline studies conducted in other countries. The experiment's results reveal that straightforward machine learning and statistical techniques, such as ARIMA, linear regression, and random forest, outperform deep learning methods like MLP and LSTM when forecasting univariate time series datasets.

 © 2023 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords: Aquaculture industry, Predictive modeling, Machine learning techniques, Marine fish production, Brunei Darussalam

 Article History: Received 13 December 2022, Received in revised form 18 April 2023, Accepted 19 May 2023

 Acknowledgment 

The authors would like to acknowledge the Department of Fisheries from the Ministry of Primary Resources and Tourism for providing the data to be used in this research. The authors would also like to thank UTB for the Centre grant to fund the whole aquaculture project under the Centre of Innovative Engineering.

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Nazmi H, Siau NZ, Bramantoro A, and Suhaili WS (2023). Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniques. International Journal of Advanced and Applied Sciences, 10(7): 109-126

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 Figures

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 Tables

 Table 1 Table 2 Table 3 Table 4 Table 5 Table 6 Table 7 Table 8 Table 9 Table 10 Table 11 Table 12 Table 13 Table 14 

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