基于ISSA-LSTM的黄鳝池溶氧量多参数预测

Multi-Parameter Prediction of Dissolved Oxygen in Eel Ponds Based on ISSA-LSTM

为提高溶氧量的多参数预测精度,文中基于改进的麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)与长短期记忆神经网络(Long and Short-Term Memory Neural Networks,LSTM)建立ISSA-LSTM溶氧量预测模型,并将该模型用于上海市农业科学院黄鳝养殖池溶氧量预测。利用混沌映射、透镜成像反向学习、自适应调节和柯西变异对麻雀搜索算法进行优化,通过小波变换进行数据预处理,并利用主成分分析法确定模型训练的输入参数。训练结果表明,相关系数、均方根误差、均方误差和平均绝对误差分别为0.911、1.392 mg·L^(-1)、1.938 mg·L^(-1)和0.992 mg·L^(-1),均优于对照模型。选择模型输入参数对模型预测结果也会产生影响,使用与溶氧量中等相关和强相关的参数同时作为输入参数的模型预测效果最优。训练结果为溶氧量多参数预测模型的发展提供了新视角。

In order to improve the multi-parameter prediction accuracy of dissolved oxygen,an ISSA-LSTM(Improved Sparrow Search Algorithm-Long and Short-Term Memory Neural Networks)dissolved oxygen prediction model is developed based on the ISSA and LSTM.The model is applied to the prediction of dissolved oxygen in eel breeding ponds at Shanghai academy of agricultural sciences.The sparrow search algorithm is optimized using chaos mapping,lensing imaging backward learning,adaptive adjustment and Cauchy variation.The data are pre-processed by wavelet transform,the input parameters for model training are determined using principal component analysis.The training results show that the correlation coefficient,root mean square error,mean square error and mean absolute error are 0.911,1.392 mg·L^(-1),1.938 mg·L^(-1)and 0.992 mg·L^(-1),which are all better than those in the control model.The choice of model input parameters also have an impact on the model prediction results,with the best model predictions using both moderately and strongly correlated parameters with dissolved oxygen as input parameters.The training results provide a new perspective for the development of the dissolved oxygen multi-parameter prediction model.