Prediction of accumulated temperature in vegetation period using artificial neural network

In this paper, the theory of artificial neural network with back-propagation algorithm (BPN) is presented, and the BPN model is used to predict the accumulated temperature for Northeast China, North China, and the Huang-Huai-Hai Plain. A total of 235 records collected from 235 meteorology stations were fed into the BPN model for training and testing. The latitude, longitude and elevation of each station were used as input variables of BPN, and the accumulated temperature as output variable. Other key network parameters, such as learning rate, momentum, the number of hidden nodes, and the learning iterations, were optimized using a trial and error approach. The optimized BPN model was compared with the multiple linear regression (MLR) model. In summary, BPN model was generally more accurate than MLR model. This infers that artificial neural network models are more applicable than regression models when predicting accumulated temperature.