基于DeepA-RRELM的池塘溶解氧时空预测方法研究  

Spatio-temporal Prediction Method of Dissolved Oxygen in Ponds Based on DeepAR-RELM

作　　者：樊宇星 任妮[3] 田港陆 段青玲[1,2] FAN Yuxing;REN Ni;TIAN Ganglu;DUAN Qingling(College of Information and Electrical Engineering,China Agricultural University,Beijing 100083,China;National Innovation Center for Digital Fishery,China Agricultural University,Beijing 100083,China;Institute of Agricultural Information,Jiangsu Academy of Agricultural Sciences,Nanjing 210014,China)

机构地区：[1]中国农业大学信息与电气工程学院,北京100083 [2]中国农业大学国家数字渔业创新中心,北京100083 [3]江苏省农业科学院农业信息研究所,南京210014

出　　处：《农业机械学报》2020年第S01期405-412,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：江苏省农业科技自主创新资金项目(CX(19)1003);山东省重大科技创新工程项目(2019JZZY010703)。

摘　　要：水体溶解氧(Dissolved oxygen,DO)是养殖水产品健康生长的重要生态因子。池塘溶解氧易受多种因素的影响,会产生时间和空间上分布的差异,现有的溶解氧预测方法大多是针对单监测点的时间序列预测,无法描述池塘溶解氧的空间分布,因此,对池塘溶解氧进行时间和空间预测非常重要。本文提出一种基于自回归循环神经网络(Autoregressive recurrent neural network,DeepAR)和正则化极限学习机(Regularized extreme learning machine,RELM)的池塘溶解氧时空预测方法。首先采用样本熵(Sample entropy,SE)衡量各个监测点溶解氧序列的波动程度,采用最大互信息系数(Maximum mutual information coefficient,MIC)衡量监测点溶解氧序列之间的相关性,综合选取出溶解氧序列波动程度较小且与各个监测点相关性较大的监测点作为中心监测点,并以中心监测点为原点,建立池塘空间坐标系;其次采用DeepAR算法构建中心监测点的溶解氧时间序列预测模型,实现中心监测点溶解氧时间序列预测;最后采用RELM算法构建中心监测点与池塘各位置溶解氧之间的空间映射关系模型,结合中心监测点溶解氧时间序列预测值和池塘空间坐标,实现对未来时刻池塘溶解氧的空间预测。该方法在提高时间序列预测精度的同时,实现了对未来时刻池塘溶解氧空间状态的预测。在真实的数据集上进行测试,预测未来24 h的池塘空间溶解氧值,均方根误差(RMSE)为1.2633 mg/L、平均绝对误差(MAE)为0.9755 mg/L、平均绝对百分比误差(MAPE)为14.8732%。并与标准极限学习机(Extreme learning machine,ELM)、径向基神经网络(Radial basis function neural network,RBFNN)、梯度提升回归树(Gradient boosting regression tree,GBRT)和随机森林(Random forest,RF)4种预测方法进行对比,各评价指标的性能均有较大幅度提升,表明该方法有较好的预测精度和泛化能力,能够较准确地实现池塘溶解氧时空预Dissolved oxygen(DO)in water is an important ecological factor for the healthy growth of aquaculture products.Dissolved oxygen in ponds is susceptible to many factors,which would cause differences in temporal and spatial distribution.Most of the existing dissolved oxygen prediction methods are time series predictions for a single monitoring point,and it cannot describe the spatial distribution of dissolved oxygen in the pond.Therefore,it is very important to predict the spatial and temporal dissolved oxygen in ponds.A spatio-temporal prediction method of dissolved oxygen in ponds based on autoregressive recurrent neural network(DeepAR)and regularized extreme learning machine(RELM)was proposed.Firstly,according to the sample entropy(SE)of the original dissolved oxygen sequence of each monitoring point and the maximum mutual information coefficient(MIC)between the sequences,a monitoring point with a smaller entropy value and a greater correlation with each point was selected as the central monitoring point,and the pond spatial coordinate system was established with the central monitoring point as the origin.Secondly,the DeepAR algorithm was used to predict the time series of dissolved oxygen in the central monitoring point.Finally,the RELM algorithm was used to construct the spatial mapping relation model between the central monitoring point and the dissolved oxygen in each location of the pond,and the spatial prediction of the dissolved oxygen in the pond in the future was realized by combining the predicted value of the time series of the dissolved oxygen at the central monitoring point and the spatial coordinates of the pond.This method not only improved the accuracy of time series prediction,but also realized the spatial prediction of dissolved oxygen in ponds.Tested on a real dataset predicting the dissolved oxygen value of the pond space in the next 24 hours,the root mean square error(RMSE)was 1.2633 mg/L,the average absolute error(MAE)was 0.9755 mg/L,and the average absolute percentage error(MAPE)was 14.8732

关 键 词：池塘养殖 溶解氧 时空预测 自回归循环神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]