An ensembled method for predicting dissolved oxygen level in aquaculture environment

Dachun Feng; Qianyu Han; Longqin Xu; Ferdous Sohel; Shahbaz Gul Hassan; Shuangyin Liu

doi:10.1016/j.ecoinf.2024.102501

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An ensembled method for predicting dissolved oxygen level in aquaculture environment

Journal article

Open access

Peer reviewed

An ensembled method for predicting dissolved oxygen level in aquaculture environment

Dachun Feng, Qianyu Han, Longqin Xu, Ferdous Sohel, Shahbaz Gul Hassan and Shuangyin Liu

Ecological informatics, Vol.80, 102501

2024

DOI: https://doi.org/10.1016/j.ecoinf.2024.102501

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Abstract

Aquaculture monitoring

Dissolved oxygen level estimation

Water quality assessment

Dissolved oxygen (DO) level is an important indicator aquaculture quality. This study proposes an ensembled method, WTD-GWO-SVR, combining wavelet threshold denoising (WTD), grey wolf optimization (GWO), and support vector regression (SVR) for accurately predicting DO levels. Addressing challenges such as high noise, poor data quality, and non-linearity and non-stationary properties of time series data, our method integrates SVR for regression-based estimation, WTD for data denoising, and GWO for optimizing the SVR parameters and the Gaussian kernel's radial basis function. We collected a dataset using a variety of low-cost sensors in a real aquaculture setting. Our comprehensive evaluation on the dataset demonstrates that WTD-GWO-SVR achieved mean squared error, mean absolute error, and R2 values of 0.38%, 3.81%, and 99.73%, respectively. It also consistently outperformed the back-propagation neural network and the long short-term memory model. It also achieved superior computational time performance compared to these methods. The high throughput and accuracy of WTD-GWO-SVR make it a potential choice for DO level prediction in water quality monitoring systems.

Details

Title: An ensembled method for predicting dissolved oxygen level in aquaculture environment
Authors/Creators: Dachun Feng - Zhongkai University of Agriculture and Engineering
Qianyu Han - Zhongkai University of Agriculture and Engineering
Longqin Xu - Zhongkai University of Agriculture and Engineering
Ferdous Sohel - Murdoch University, Centre for Crop and Food Innovation
Shahbaz Gul Hassan - Zhongkai University of Agriculture and Engineering
Shuangyin Liu - Zhongkai University of Agriculture and Engineering
Publication Details: Ecological informatics, Vol.80, 102501
Publisher: Elsevier B.V
Identifiers: 991005637970107891
Murdoch Affiliation: School of Information Technology
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 3 Agriculture, Environment & Ecology; 3.91 Contamination & Phytoremediation; 3.91.1064 Sediment Metal Risks
Web Of Science research areas: Ecology
ESI research areas: Environment/Ecology