基于EMD-IGA-SELM的池塘养殖水温预测方法

Water Temperature Prediction in Pond Aquaculture Based on EMD-IGA-SELM Neural Network

为了有效指导工厂化水产养殖,提高水产养殖过程中水体温度预测的精度和稳定性,在分析水体温度影响因素的基础上,提出基于经验模态分解(EMD)、改进遗传算法(IGA)和改进极限学习机(SELM)相结合的水温预测模型(EMD-IGA-SELM)。首先,通过综合天气指数的计算完成异常和缺失数据的校正;利用皮尔森相关分析计算各影响因子与水温之间的相关度,从而确定预测模型的输入输出量;选择Softplus函数代替Sigmoid函数组成SELM,并引入混沌序列改进标准遗传算法,获得SELM的最佳初始权值和阈值;最后,采用EMD方法将原始水温时序数据进行多尺度分解,在各分量中对IGA-SELM训练建模,并叠加求和各分量预测值,从而完成水温序列的预测。将EMD-ELM和GA-BP模型的预测结果与EMD-IGA-SELM进行对比,结果表明,EMD-IGA-SELM取得了较好的预测精度,评价指标平均绝对误差、平均绝对百分比误差和均方根误差分别为0. 123 3℃、0. 004 3和0. 147 8℃,能够满足水产养殖的生产需要,可为池塘水质管理和调控提供决策支持。

In order to guide the intensive aquaculture effectively and improve the accuracy and stability of water temperature prediction,based on the analysis of water temperature factors,a prediction model(EMD-IGA-SELM)was proposed with the combination of empirical mode decomposition(EMD),improved genetic algorithm(IGA)and improved extreme learning machine(SELM).Firstly,the outlier and missing data were corrected with the calculation of composite meteorological index.Secondly,the Pearson correlation was utilized to explore the relationships between affecting factors and water temperature,and construct the input and output of prediction model.Then,Softplus function was used as activation function of SELM to replace Sigmoid.The best weight and threshold of SELM were obtained from the IGA,which introduced the chaotic sequence to traditional GA.Finally,EMD algorithm was applied to decompose the original water temperature time series into a series of intrinsic mode function(IMF).IGA SELM prediction models were trained in each IMF sequence,and the predicted values were calculated by the sum of predicted value in each IMF sequence.The experimental results showed that EMD-IGA-SELM had better prediction accuracy,and the mean absolute error(MAE),mean absolute percentage error(MAPE)and root mean square error(RMSE)of GA SELM were 0.123 3℃,0.004 3 and 0.147 8℃,respectively.Research results met the practical needs of the aquaculture and provided decision support for water quality management and control.