Random Forest Feature Selection and POA-LSTM Combination Based Prediction Method for Reference Evapotranspiration
In order to better capture the nonlinear characteristics and effective influencing factors of reference crop evapotranspiration(ET0)data,and to achieve accurate ET0 prediction when meteorological information is lacking,an ET0 prediction method based on the fusion modeling idea of a combination of Random Forest Feature Selection and Pelican Optimization Algorithm(POA)optimized Long Short Term Memory Neural Network(LSTM)is proposed.First,Random forest feature selection was used to evaluate the importance of the features and filter the effective weather factors as model inputs;subsequently,the optimal hyperparameter combinations are searched by POA for optimizing the LSTM model;finally,the ET0 prediction was performed based on the LSTM model under the optimal hyperparameters.The results show that the POA-LSTM model outperformed the other models,among which POA-LSTM1(u2、N、RH、Tmean)has the highest prediction accuracy,with the test set R2,RMSE and MAE of 0.927,0.778,and 0.400 mm/d,respectively;POA-LSTM4(u2、N)also demonstrated good performance in estimating ET0 with fewer meteorological inputs,with the test set R2,RMSE and MAE of 0.881、0.995 and 0.510 mm/d,with higher prediction accuracy and stability compared to other methods.
reference evapotranspirationlong short term memoryrandom forestsfeature selectionpelican optimization algorithm
万方数据
维普
