文献类型：期刊文献

中文题名：IFWA优化的BLSTM与transformer融合构建海水水质三维预测模型

英文题名：IFWA-optimized BLSTM and transformer fusion for constructing 3D prediction models for seawater quality

作者：谢再秘[1,2];王骥[2,3];莫春梅[2,3]

机构：[1]广东海洋大学数学与计算机学院,湛江524088;[2]广东省智慧海洋传感网及其装备工程技术研究中心,湛江524088;[3]广东海洋大学电子与信息工程学院,湛江524088

年份：2023

卷号：39

期号：4

起止页码：162

中文期刊名：农业工程学报

外文期刊名：Transactions of the Chinese Society of Agricultural Engineering

收录：CSTPCD、、EI(收录号：20232714360390)、CSCD2023_2024、Scopus、北大核心、CSCD、北大核心2020

基金：广东省普通高校重点领域新一代信息技术专项(2020ZDZX3008);广东省人工智能领域重点专项(2019KZDZX1046)。

语种：中文

中文关键词：主成分分析;模型;互信息;三维海水水质;MIPCA-BLSTM-transformer-IFWA模型

外文关键词：principal component analysis;models;mutual information;three-dimensional seawater quality;MIPCA-BLSTMtransformer-IFWA model

中文摘要：为了精准揭示不同水层各个时间序列的海域养殖水质参数含量在三维空间的变化规律,该研究首先在海水水质数据处理方面提出了将主成分分析算法(principal component analysis,PCA)与互信息算法(mutual information,MI)相融合的数据处理算法(MIPCA);其次将双向长短期记忆(bidirectional long short-term memory,BLSTM)与transformer 2个网络融合提出了新模型,即首先利用改进烟花算法(improved fireworks algorithm,IFWA)优化了双向长短期记忆神经网络中超参数;再利用transformer中注意力机制关注重要水质特征,最后将两个网络集成提出了混合模型MIPCA-BLSTM-transformer-IFWA。试验结果表明,该模型在MAPE(mean absolute percentage error)、RMSE(root mean square error)、R(coefficient of correlation)和D(willmott index of agreement)的4个度量指标分别为0.075、0.116、0.960和0.997,优于IPSO-KLSTM等传统预测模型。该研究提出的混合模型能够准确揭示海水中不同水层的无机氮含量、活性磷酸盐含量和pH值等参数变化趋势。

外文摘要：The water environment can be closely related to the living,food,and oxygen for seawater in aquaculture.However,the rapid increase of inorganic nitrogen and active phosphate phosphorus content has indirectly led to the abnormally severe eutrophication of seawater in recent years,due to human activities,environmental pollution,and agricultural production.Water eutrophication has been found in Zhanjiang Bay in China,which is extremely harmful to mariculture.There is some change in the pH and other indicators in the cultured water,which in turn affects the growth of aquatic organisms.Therefore,it is necessary to use real-time monitoring and predict the water quality parameters in the aquaculture process,and then evaluate the quality of aquatic products.This study aims to accurately reveal the mariculture water quality in three-dimensional space for each time series of different water layers.A three-dimensional prediction model was constructed to optimize the water quality time series.Principal component analysis(PCA)was first conducted to screen out the key parameters of water quality.The mutual information(MI)was combined to analyze the non-linear relationship between the key parameters of water quality and output parameters.The water quality parameters were selected with the large correlation as the input of bidirectional long short-term memory(BLSTM)in the feature extraction module using the improved fireworks on BLSTM in hyperparameters for optimization.The learned network was hidden the state vector input into the transformer to focus on more water quality information.The prediction model was trained for the water quality inorganic nitrogen concentration,active phosphate concentration,and pH value for the three-dimensional prediction.The research subject was taken as the sea area in Xuwen County,Zhanjiang City,Guangdong Province,China.The improved model was validated using 13 typical online monitoring stations from June to August 2022,as well as three handheld detection stations with hourly collected data sets.The experimental results show that:1)The water quality prediction model with the MIPCA-BLSTM-transformer-IFAW outperformed the SC-K-means-RBF,RS-GBRT,3D cyclic,BRT,M-ConvLSTM,and IPSO-KLSTM prediction model in four metrics of MAPE,RMSE,R and D,where the ICEEMD-BLSTM prediction models were four metrics of 0.075,0.116,0.96 and 0.997,respectively.2)IFAW improved the model prediction performance better than the CS,GA,PSO,and Jaya algorithm.3)The content of inorganic nitrogen and reactive phosphate increased with the increase in the water depth,whereas,the pH value decreased.The hybrid model can be expected to accurately reveal the trends of inorganic nitrogen concentration,reactive phosphate concentration,and the pH value in different water layers in seawater as well.The next work can continue to simplify the network structure for better model robustness.