CHLOROPHYLL A CONCENTRATION PREDICTION MODEL WITH MRMR-SA-EGA-ELM
To improve the prediction accuracy of chlorophyll a concentration,taking the water quality monitoring data of the South Taihu Lake area-Xintang harbour of Huzhou City from May to November 2020 as the original sample data,the maximum relevance minimum redundancy algorithm was used to select better feature values from the original sample data as the input data for the prediction model.The combination of elite genetic algorithm and simulated annealing algorithm was used to optimize the initial parameters in the extreme learning machine network.The prediction model of chlorophyll a concentration with MRMR-SA-EGA-ELM was constructed.The experimental results show that the mean absolute error,mean square error and determination coefficient of the MRMR-SA-EGA-ELM model predicting chlorophyll a concentration are 1.009,1.607,0.903 respectively,while the MAE,MSE and R2 of the ELM model are 2.078,8.249,and 0.562 respectively.The effect of the MRMR-SA-EGA-ELM model is significantly improved,and the accurate prediction of chlorophyll a concentration can be achieved.
国家科技期刊平台
NSTL
万方数据
维普
