## 4 Discussion
### Timing Anomaly Detection  
The depth model of MLP had a good learning ability and prediction accuracy with five input features, but the estimation ability of the NEE was still lower than that of the Reddynproc model on the whole. Considering that the Reddynproc algorithm includes outlier detection technology as can be seen from Figure 3, the outlier detection algorithm based on the NEE data outlier detection and elimination of the data dispersion was reduced, and the data quality was improved. [PERSON] et al. (2006) [2] performed quality checks and filtering based on the relationship between the measured flux and friction velocity, using the absolute bias median of the outlier estimator (MAD) for outlier detection on the basis of a double-difference time series, to discard biased data. The problem of anomaly detection has been widely studied in various fields and applications. [PERSON] et al. (2009) [43] and [PERSON] et al. (2017) [44] pointed out that anomaly detection aims at finding unusual data values or patterns in time series data that do not satisfy the normal constraint rules of a given model, and, in many practical applications, it has certain practical value.  
In order to explore the importance of time series anomaly detection in NEE model prediction, the training was carried out after the time series anomaly value of the NEE was eliminated. The outliers here were timestamp values where the observed NEE was different from the expected NEE of the time series. When a particular time instant was compared with the other values in the time series (global outliers) or with its neighbors (local outliers), it presented an anomaly. IOR was used to set the threshold and to judge the outliers by the threshold. The red color indicated the outliers. Figure 9 shows the training curve of the MLP training model before and after NEE timing anomaly detection; the accuracy curve of the MLP prediction model in the test set was lower than that in the training set, which showed that the model did not perform well in the test set, and the model determination coefficient R\({}^{2}\) was 0.58. The prediction accuracy of the MLP prediction model in the test set was better, and the model determination coefficient R\({}^{2}\) was raised to 0.64 (Figure 10), which showed that the elimination of the time series outliers had a great influence on NEE simulation. Stable laminae, which were often present at night, inhibited turbulence, leading to an underestimation of the nocturnal NEE, that is, ecosystem respiration ([PERSON] et al., 2007) [45], as [PERSON] and [PERSON] (2002) [46] suggested. Specifically, adverse conditions could be detected by examining the relationship between the nocturnal NEE and u*, and Reddynproc provided this method, which allowed the determination of the nighttime friction wind velocity thresholds under conditions that were not considered as such [42; 43]. The nighttime flux could be estimated accurately. Machine learning (ML) and deep learning (deep learning) lacked the understanding of the mechanism between the variables; however the data parameters were required to be low, so they were not as good as Reddynproc in the estimation of the NEE. The results further showed that the understanding of the underlying mechanism of the NEE was of great significance to the prediction model.