基于烟花算法优化极限学习机的温室参考作物蒸散量预测研究

为提高温室环境下参考作物蒸散量(Reference Crop Evapotranspiration,ET0)的预测精度,提出烟花算法(Fireworks Algorithm,FWA)优化极限学习机(Extreme Learning Machine,ELM)的参考作物蒸散量预测模型,有效解决了极限学习机在数据预测过程中因随机输入权值矩阵和偏置矩阵导致的数据波动问题,提高了极限学习机的预测精度。以温室环境数据作为模型的输入,以参考作物蒸散量ET0为输出,建立FWAELM模型,并将结果与ELM模型预测结果进行对比,结果表明,FWAELM模型的均方根误差、平均绝对误差和模型可决系数分别为:0.1156、0.1436、0.9438,高于ELM模型的0.4035、0.3467和0.8190,FWAELM模型预测精度较高。同时进行了气象参数缺失情况下的模型预测精度研究,结果表明参数在保留3个及以上时,模型的预测精度依然较高,适用于温室ET0的预测研究。

A Prediction of Greenhouse Reference Evapotranspiration Forecasting Based on Fireworks Algorithm Optimized Extreme Learning Machine

In order to improve the prediction accuracy of the Reference Crop Evapotranspiration(ET0)in the greenhouse environment,Fireworks Algorithm(FWA)is proposed to optimize the crop reference evapotranspiration model established by Extreme Learning Machine(ELM),which effectively solves the data fluctuation problem caused by the random input weight matrix and the bias matrix of the Extreme Learning Machine in the process of data prediction,improves the prediction accuracy of Extreme Learning Machine.By taking the greenhouse environment data as the input of the model,the FWAELM model is established with reference evapotranspiration ET0 as the output,and the results are compared with the ELM model prediction results.The outcome shows that the root mean square error,mean absolute error and model deterministic coefficients of the FWAELM model are:0.1156,0.1436,0.9438,better than ELM's 0.4035,0.3467 and 0.8190,FWAELM model has higher prediction accuracy.In addition,the prediction accuracy of the model under the absence of meteorological parameters is studied.The results show that the prediction accuracy of the model is still high when the parameters are three or more,which is suitable for the prediction research on greenhouse ET0.