Research on a Pearson-LSTM-AM-based water quality prediction model for freshwater aquaculture

IF 2.3 3区 农林科学 Q2 FISHERIES
Wujia Yu, Minghao Wu, Zhenzhou Ha
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引用次数: 0

Abstract

In the field of freshwater aquaculture, water quality significantly impacts the aquaculture products. Fluctuations in water quality can hinder the growth of cultured organisms, lead to frequent diseases, and even cause mass mortality. Therefore, accurately predicting water quality is crucial. To reduce the error rate that may occur when using traditional long short-term memory networks (LSTM) models for water quality prediction, this article proposes a Pearson-LSTM-AM water quality prediction model. Initially, the Pearson correlation test algorithm is used for the input feature selection, and then an attention mechanism is integrated to enhance the LSTM neural network's ability to learn the key features, specifically for predicting the dissolved oxygen (DO) indicator of water quality. Experimental results demonstrate that the proposed method significantly improves upon the Pearson-LSTM model and the LSTM model in terms of root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), and the coefficient of determination (R2) metrics.

Abstract Image

基于pearson - lstm - am的淡水养殖水质预测模型研究
在淡水养殖领域,水质对水产养殖产品影响显著。水质的波动会阻碍培养生物的生长,导致频繁的疾病,甚至造成大量死亡。因此,准确预测水质至关重要。为了降低传统长短期记忆网络(LSTM)模型用于水质预测时可能出现的错误率,本文提出了一种Pearson-LSTM-AM水质预测模型。首先使用Pearson相关检验算法进行输入特征选择,然后集成注意机制增强LSTM神经网络学习关键特征的能力,特别是用于预测水质溶解氧(DO)指标。实验结果表明,该方法在均方根误差(RMSE)、平均绝对百分比误差(MAPE)、平均绝对误差(MAE)和决定系数(R2)等指标上都明显优于Pearson-LSTM模型和LSTM模型。
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来源期刊
CiteScore
5.90
自引率
7.10%
发文量
69
审稿时长
2 months
期刊介绍: The Journal of the World Aquaculture Society is an international scientific journal publishing original research on the culture of aquatic plants and animals including: Nutrition; Disease; Genetics and breeding; Physiology; Environmental quality; Culture systems engineering; Husbandry practices; Economics and marketing.
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