摘要
为了提高水产养殖中溶解氧的预测精度,该文提出了基于集合经验模态(ensemble empirical mode decomposition,EEMD)分解、游程检测法重构、适宜的单项预测算法建模和BP神经网络非线性叠加的组合预测模型。该模型首先将溶解氧原始序列用EEMD分解法进行分解,得到了多个分量;其次,用游程检测法将这些分量重构成高频分量、中频分量和低频分量等3个分量;接着,针对高频分量波动性大且复杂、中频分量呈现周期性、低频分量几乎呈线性的特点,采用粒子群(particle swarm optimization,PSO)优化的最小二乘支持向量机(least square support vector machine,LSSVM)对高频项进行预测,采用极限学习机(extreme learning machine,ELM)对中频项预测,采用非线性回归(nonlinear regression method,NRM)对低频项预测;最后,将3个分量预测的结果用BP神经网络进行重构得到最终的预测结果。将该模型应用于江苏省溧阳市埭头黄家荡特种水产养殖场的溶解氧预测中,试验表明,该种以游程检测法重构EEMD为基础的混合预测模型的预测精度高于PSO-LSSVM和单一的ELM预测模型。在预测未来48 h的溶解氧值时,该模型的预测值与实测值的均方根误差RMSE为0.099 2、平均相对误差均值MAPE为0.078、平均绝对误差MAE为0.015 5,R^2为0.995 5。表明该模型有较好的预测精度和泛化能力,能够满足现代化水产养殖业对溶解氧精细化管理的高要求。
Dissolved oxygen(DO)is one of the key water quality parameters for water products,which reflects changes in water quality in aquaculture.To increase prediction accuracy of dissolved oxygen(DO)in aquaculture,a hybrid model based on ensemble empirical mode decomposition(EEMD),reconstruction using runs test method,the appropriate forecasting method and superposition by BPANN was proposed.Firstly,the dissolved oxygen sequence was decomposed into a series of components by EEMD.EEMD performs very well in noise reduction and detail features extraction.Original dissolved oxygen datasets were decomposed to 9 IMFs and a Res.Then these components were reconstructed into high frequency component,intermediate frequency and low frequency component by the runs test method,which centralized the characteristic information and reduced the difficulty of predicting.Thirdly,based on the changed characteristics of each sequence,the appropriate prediction method was selected.According to the characteristics of high frequency component,which fluctuated violently and varied complicatedly,the least squares support vector machine(LSSVM)optimized by particle swarm optimization(PSO)was used to predict this term because this method had high accuracy and adaptability to predict.Intermediate frequency component was forecasted by extreme learning machine(ELM)because this component presented a periodic characteristic.The fluctuation of low frequency term was gentle and periodic,so it could be modeled by nonlinear regression method.Finally,the predicted values of DO datasets were calculated by using BPANN to reconstruct the forecasting values of all components,which ensured good fitting effect and stability.The model adopted BPANN nonlinear superposition to replace simple adaptive superposition,which achieved better fitting effect.This model was tested in Daitou Huangjiadang Special Aquaculture Farm in Liyang City,Jiangsu Province.Based on the prediction model,the dissolved oxygen changing was predicted for aquaculture pond during June 23 to June
作者
宦娟
曹伟建
秦益霖
吴帆
Huan Juan;Cao Weijian;Qin Yilin;Wu Fan(School of Information Science&Engineering,Changzhou University,Changzhou 213164,China;Changzhou Technical Institute of Tourism&Commerce,Changzhou 213023,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2018年第8期220-226,共7页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金(61772090)
溧阳市第一批重点研发计划(现代农业)项目(LB2016003)
关键词
水质
水产养殖
模型
溶解氧
集合经验模态分解
游程检测法
组合预测
water quality
aquaculture
models
dissolved oxygen
ensemble empirical mode decomposition
runs test method
combinatorial prediction model
