## 3 Results and Discussion  
Various meteorological parameters used for different input combination are the crucial factor for prediction accuracy. The SVMs models with different input combinations showed different prediction accuracy. The results indicated that, when using all the 32 micrometeorological parameters, the prediction accuracy was the best by using the SVM-BRF model. The prediction accuracy of training set and testing set was 98.77% and 90.29%, respectively. However, when only using single type and linear data such as the temperature, humidity and radiation data, simple SVM without any functions showed highest prediction accuracies both of training and testing sets compared with the SVM-linear, SVM-BRF and SVM-polynomial models. As temperature data used for training, the prediction accuracy of training and testing set was 84.07% and 90.29%, respectively. For the humidity data, it was 84.07% and 91.43%, respectively. For the radiation data, it was 81.37% and 92.00%, respectively. As the soil data such as \(T_{soil}\) and \(G\) applied in the five SVMs models, the SVM-BRF model showed the best prediction accuracy compared with the other four models. The prediction accuracy of training and testing set was 80.88% and 90.29%, respectively (Figure 2). The accuracy of the test set was always a bit higher than that of the training set, which was most likely due to the problem of data distribution, that is, the distribution of the training set was inconsistent with that of the testing set. One of the reasons is the containing problems in the data distribution. The data of the testing set were included in the data of the training set. The other reason is the large intersection problem of the data distribution. That is because the non-intersection part of training data distribution is wider than the testing data distribution. Those two reasons both cause the data of the testing set to be simpler than that of the training set. Thus, the prediction accuracy of the testing set might also be higher than that of the training set. Frosts have been defined as advection frost and radiation frost [23]. Advection frosts happened frequently happen in a cold and windy weather, which is associated with large-scale and strong cold air with the temperature below 0 \({}^{\circ}\)C. Radiation frosts are caused by the radiant energy loss from the atmosphere, soil and plant ecosystem in the clear, no-wind nights. It will cause peculiar temperature inversions phenomenon [24; 25]. Air temperature and air relative humidity are the most vital micrometeorological factors for the formation of the radiation frost [26]. In general, at a radiation frost night, the air relative humidity is normally higher than 90%. Dew-point temperature and air temperature are typically below 0 \({}^{\circ}\)C [27; 28]. That is because high humidity air contacts with a cooler surface, water vapor will condense on the radiated surface. With further decrease of the air temperature to lower than the freezing point, frost will form on the surface [2; 11]. There is a functional relationship between humidity and air temperature, they also changed with the heights with a polynomial function. Canopy of the plant is the most sensitive part for the freezing injury and frost damage [2; 24; 29]. Plant canopies intercept energy radiated from the land surface to the atmosphere and changes the air flow capacity as well.