基于PC-RELM的养殖水体溶解氧数据流预测模型  

Data stream prediction model for dissolved oxygen in aquaculture water using PC-RELM

作　　者：施珮 匡亮[3] 王泉 袁永明 SHI Pei;KUANG Liang;WANG Quan;YUAN Yongming(Wuxi University,Wuxi 214105,China;Jiangsu Internet of Things Device Super Convergence and Security Research Center,Wuxi 214105,China;School of IoT Engineering,Jiangsu Vocational College of Information Technology,Wuxi 214153,China;Freshwater Fisheries Research Center,Chinese Academy of Fishery Sciences,Wuxi 214081,China)

机构地区：[1]无锡学院,无锡214105 [2]江苏省物联网设备超融合与安全工程研究中心,无锡214105 [3]江苏信息职业技术学院物联网工程学院,无锡214153 [4]中国水产科学研究院淡水渔业研究中心,无锡214081

出　　处：《农业工程学报》2023年第7期227-235,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：江苏省高校自然科学研究面上项目(21KJB520020);无锡市“太湖之光”科技攻关项目(K20221044);国家自然科学基金项目(62072216);南京信息工程大学滨江学院人才启动经费资助项目(2021r038);江苏省教育科学“十四五”规划2021年度课题(B/2021/01/15)。

摘　　要：养殖水体中溶解氧浓度一直是最重要的水质参数之一。为了精准地对水体溶解氧进行调控,提高养殖生产效率,降低养殖风险,该研究考虑外部天气条件对溶解氧的影响以及溶解氧自身的昼夜变化特征,提出一种基于正则化极限学习机(principal component analysis and clustering method optimized regularized extreme learning machine,PC-RELM)的养殖水体溶解氧数据流预测模型。首先,采用主成分分析法判断影响溶解氧浓度的强重要性因子,降低预测模型的数据维度;其次,利用熵权法计算各时刻点的天气环境指数,并利用快速动态时间规整算法(fast dynamic time warping,FastDTW)完成时间序列数据流在不同天气环境下的相似度度量;然后使用k-means算法对时间序列的相似度进行聚类分簇,并基于分簇结果完成正则化极限学习机预测模型的构建,实现溶解氧浓度的估算。最后将PC-RELM模型应用到无锡南泉试验基地养殖池塘的溶解氧预测调控过程中。试验结果表明:PC-RELM的预测均方根误差值(root mean square error,RMSE)为0.9619,与PLS-ELM(partial least squares optimized ELM)、最小二乘支持向量机(least square support vector machine,LSSVM)以及BP神经网络模型进行对比,其RMSE值分别降低了41.54%、54.58%和67.16%。该预测模型可以有效地捕捉不同天气条件下溶解氧的变化特点,具有较高的预测精度和效率。Dissolved oxygen(DO)is one of the most important parameters for the water quality in aquaculture water.Long-term low oxygen environment can dominate the growth and reproduction of fish.Hypoxia can also cause large areas of fish death.Accurate and efficient DO prediction and control strategies can improve aquaculture production efficiency for the fewer aquaculture risks.However,an effective DO prediction has always been a tough challenge in aquaculture,due to the interference of external weather and the DO complexity.Multi-source or single sensors are generally used to build the prediction models,without considering the DO characteristics under similar weather conditions.Particularly,there is an outstanding diurnal variation in the DO content.Moreover,some redundant data can be collected from the water quality sensors in automatic weather stations.In this study,the principal component analysis and clustering method optimized regularized extreme learning machine(PC-RELM)was proposed to realize the DO prediction,considering the influence of external weather conditions on the DO and the diurnal variation.Firstly,the principal component analysis(PCA)was applied to determine the most influencing factors on the DO concentration,and reduce the data dimension of the prediction model for the high efficiency of prediction;Secondly,the entropy weight method was utilized to calculate the weather environment index at different time points.Fast dynamic time warping(FastDTW)was used to measure the similarity of weather environment in the time series data streams;Then,the K-means algorithm was used to cluster the similarity of the time series using the weather environment index.And the sub-prediction models of regularized extreme learning machine(RELM)were constructed using the clustered datasets to forecast the DO concentration.Finally,the PC-RELM model was applied to the intelligent control process of DO in the aquaculture pond of the Wuxi Nanquan experimental base.The test results showed that the root-mean square error(RMSE)of

关 键 词：溶解氧 养殖 水质 聚类 快速动态时间规整算法 正则化极限学习机 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TP212[自动化与计算机技术—计算机科学与技术] TP274.2