Prediction of sap flow with historical environmental factors based on deep learning technology

Abstract

Sap flow is an important intermediate link that reflects the continuous soil–plant-atmosphere cycle. Therefore, it is important to predict the sap flow to analyze the amount of tree transpiration for assessing of water consumption. In this paper, we propose a new sap flow assessment using environmental factors based on a convolutional neural network-gated recurrent unit (CGRU) hybrid deep learning method. The model was trained and tested with the sap flow and environmental factors from 17,568 group observations from public SAPFLUXNET dataset. These group observations measured from January 1, 2012 to December 31, 2012, with acquisition interval of 30 min for one tree of New Zealand Agathis australis. After designed the CGRU structure by integrated a convolutional neural network (CNN) and a gated recurrent unit (GRU) neural network, the input variables were selected with a correlation analysis between sap flow and environmental factors. Additionally, the number of previous conditions were introduced into the input of the model. Results showed that when the number of previous conditions set to 16, the learning rate set to 0.01 with Adam optimization algorithm, the mean squared error, mean absolute percentage error, and coefficient of determination of the CGRU model were 0.00231, 22.31 and 0.948 respectively. Comparing results showed that the CGRU-based sap flow prediction model has more accuracy than other eight models participating in the test including the independent CNN, GRU, CNN-Long short-term memory (LSTM) and five traditional machine learning based models. The least time spent on training is also the CGRU model. The CGRU-based sap flow prediction model proposed in this paper can capture complex nonlinear dependencies and yield accurate assessments of sap flow. Our model we established in this paper can be useful for research on forest stand transpiration, water consumption and the soil–plant-atmosphere cycle.