School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.
Co-innovation Center of Shandong Colleges and Universities: Future Intelligent Computing, Shandong Technology and Business University, Yantai, China.
Math Biosci Eng. 2021 Sep 2;18(6):7561-7579. doi: 10.3934/mbe.2021374.
In the field of intensive aquaculture, the deterioration of water quality is one of the main factors restricting the normal growth of aquatic products. Predicting water quality in real time constitutes the theoretical basis for the evaluation, planning and intelligent regulation of the aquaculture environment. Based on the design principles of decomposition, recombination and integration, this paper constructs a multiscale aquaculture water quality prediction model. First, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the different water quality variables at different time scales step by step to generate a series of intrinsic mode function (IMF) components with the same characteristic scale. Then, the sample entropy of each IMF component is calculated, the components with similar sample entropies are combined, and the original data are recombined into several subsequences through the above operations. In this paper, a prediction model based on a long short-term memory (LSTM) neural network is constructed to predict each recombination subsequence, and the Adam optimization algorithm is used to continuously update the weight of neural network to train and optimize the prediction performance. Finally, the predicted value of each subsequence is superimposed to predict the original water quality data. The dissolved oxygen and pH data of an aquaculture base were collected for prediction experiments, the results of which show that the proposed model has a high prediction accuracy and strong generalization performance.
在集约化水产养殖领域，水质恶化是限制水产养殖正常生长的主要因素之一。实时预测水质是水产养殖环境评价、规划和智能调控的理论基础。本文基于分解、重组和集成的设计原则，构建了一个多尺度水产养殖水质预测模型。首先，采用完备集合经验模态分解自适应噪声（CEEMDAN）方法，逐步分解不同时间尺度的水质变量，生成一系列具有相同特征尺度的固有模态函数（IMF）分量。然后，计算每个 IMF 分量的样本熵，将样本熵相似的分量组合起来，通过上述操作将原始数据重新组合成几个子序列。在本文中，构建了一个基于长短期记忆（LSTM）神经网络的预测模型，对每个重组子序列进行预测，并采用 Adam 优化算法不断更新神经网络的权重，以训练和优化预测性能。最后，对每个子序列的预测值进行叠加，以预测原始水质数据。对一个水产养殖基地的溶解氧和 pH 值数据进行了预测实验，结果表明，所提出的模型具有较高的预测精度和较强的泛化性能。
