Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model.
Aquaculture 4.0: hybrid neural network multivariate water quality parameters forecasting model.
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DOI：
10.1038/s41598-023-41602-7
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发表时间：
2023-09-26
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
--
作者：
研究了混合深度神经网络和多变量水质预测模型在水产养殖生态系统中的有效性。关键水质参数的准确预测可以及时识别可能的问题区域，并使决策者能够采取先发制人的补救行动，从而大大改善水产养殖业的水质管理。基于集成经验模态分解（EEMD）方法、深度学习长短期记忆（LSTM）神经网络（NN）方法和多元线性回归（MLR）方法，建立了一种新型的混合深度学习神经网络多元水质参数预测模型。所提出的水质预测模型（简称EEMD-MLR-LSTM NN模型）是使用从苏格兰西北部斯库里附近的鲑鱼近海养殖场洛赫杜阿尔特公司收集的多变量时间序列水质传感器数据开发的。通过与实测水质参数数据和养殖场真实的浮游植物计数数据的比较，验证了该混合水质预测模型的性能。结果表明，新的混合水质预测模型可以作为一个有价值的支持工具，在水产养殖业的水质管理。
This study examined the efficiency of hybrid deep neural network and multivariate water quality forecasting model in aquaculture ecosystem. Accurate forecasting of critical water quality parameters can allow for timely identification of possible problem areas and enable decision-makers to take pre-emptive remedial actions that can significantly improve water quality management in aquaculture industry. A novel hybrid deep learning neural network multivariate water quality parameters forecasting model is developed with the aid of ensemble empirical mode decomposition (EEMD) method, deep learning long-short term memory (LSTM) neural network (NN), and multivariate linear regression (MLR) method. The presented water quality forecasting model (shortened as EEMD–MLR–LSTM NN model) is developed using multivariate time-series water quality sensor data collected from Loch Duart company, a Salmon offshore aquaculture farm based around Scourie, northwest Scotland. The performance of the novel hybrid water quality forecasting model is validated by comparing the forecast result with measured water quality parameters data and the real Phytoplankton data count from the aquaculture farm. The forecast accuracy of the results suggests that the novel hybrid water quality forecasting model can be used as a valuable support tool for water quality management in aquaculture industries.
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影响因子：
3.9
作者：
Hu, Zhuhua;Zhang, Yiran;Liu, Juntao
通讯作者：
Liu, Juntao
影响因子：
2.7
作者：
Eze, Elias;Ajmal, Tahmina
通讯作者：
Ajmal, Tahmina
影响因子：
5.8
作者：
Li, Qiang;Yang, Yinqun;Wang, Yonggui
通讯作者：
Wang, Yonggui
影响因子：
3.4
作者：
Shin, Yuna;Kim, Taekgeun;Heo, Tae-Young
通讯作者：
Heo, Tae-Young
DOI：
10.1186/2052-336x-12-40
发表时间：
2014-01-23
影响因子：
3.4
作者：
Zare Abyaneh H
通讯作者：
Zare Abyaneh H