A DEEP LEARNING PROGRAM FOR PREDICTING SAP FLOW OF LARIX OLGENSIS

Abstract

Plant sap flow is crucial to understanding plant transpiration, plant hydraulic functioning and physiological properties. In this study, a method for predicting trunk sap flow of Larix olgensisusing deep learning was proposed. The method is based on the combined use of Long-short term memory network (LSTM) and transformer model, noted as LSTM-transformer model. The experimental results show that the proposed method provides more accurate prediction quality in terms of correlation coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE), compared to the state of the art forecast methods such as BP, DNN, LSTM, and transformer models.