Yang Wenbo, Liu Wei, Gao Qun
School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai 264005, China.
Key Laboratory of Sensing Technology and Control in University of Shandong, Yantai 264005, China.
Math Biosci Eng. 2023 Jan;20(1):998-1017. doi: 10.3934/mbe.2023046. Epub 2022 Oct 21.
As an essential water quality parameter in aquaculture ponds, dissolved oxygen (DO) affects the growth and development of aquatic animals and their feeding and absorption. However, DO is easily influenced by external factors. It is not easy to make scientific and accurate predictions of DO concentration trends, especially in long-term predictions. This paper uses a one-dimensional convolutional neural network to extract the features of multidimensional input data. Bidirectional long and short-term memory neural network propagated forward and backward twice and thoroughly mined the before and after attribute relationship of each data of dissolved oxygen sequence. The attention mechanism focuses the model on the time series prediction step to improve long-term prediction accuracy. Finally, we built an integrated prediction model based on convolutional neural network (CNN), bidirectional long and short-term memory neural network (BiLSTM) and attention mechanism (AM), which is called CNN-BiLSTM-AM model. To determine the accuracy of the CNN-BiLSTM-AM model, we conducted short-term (30 minutes, one hour) and long-term (6 hours, 12 hours) experimental validation on real datasets monitored at two aquaculture farms in Yantai City, Shandong Province, China. Meanwhile, the performance was compared and visualized with support vector regression, recurrent neural network, long short-term memory neural network, CNN-LSTM model and CNN-BiLSTM model. The results show that compared with other comparative models, the proposed CNN-BiLSTM-AM model has an excellent performance in mean absolute error, root means square error, mean absolute percentage error and determination coefficient.
作为水产养殖池塘中的一项关键水质参数，溶解氧（DO）影响着水生动物的生长发育及其摄食与吸收。然而，溶解氧很容易受到外部因素的影响。对溶解氧浓度趋势进行科学准确的预测并非易事，尤其是长期预测。本文采用一维卷积神经网络来提取多维输入数据的特征。双向长短期记忆神经网络向前和向后传播两次，深入挖掘溶解氧序列各数据的前后属性关系。注意力机制使模型聚焦于时间序列预测步骤，以提高长期预测精度。最后，我们构建了一个基于卷积神经网络（CNN）、双向长短期记忆神经网络（BiLSTM）和注意力机制（AM）的集成预测模型，即CNN - BiLSTM - AM模型。为了确定CNN - BiLSTM - AM模型的准确性，我们在中国山东省烟台市的两个水产养殖场对实际监测数据集进行了短期（30分钟、1小时）和长期（6小时、12小时）的实验验证。同时，将其性能与支持向量回归、递归神经网络、长短期记忆神经网络、CNN - LSTM模型和CNN - BiLSTM模型进行了比较并可视化。结果表明，与其他对比模型相比，所提出的CNN - BiLSTM - AM模型在平均绝对误差、均方根误差、平均绝对百分比误差和决定系数方面具有优异的性能。
