## 4 Discussion
### Improved MLP Deep Learning Model  
The Reddyproc model could obtain a better NEE estimation model with fewer input parameters, while the machine learning prediction model had eight input features. However, in the high-latitude and high-altitude area, the above-mentioned machine learning models were greatly limited in the study of ecosystem carbon assessment in the absence of corresponding measured data due to the limited observation elements, the harsh climate conditions, and the uneven distribution of the observation sites. Is it possible to find a model that does not require the relevant physical parameters of the high-cold region, greatly reducing the parameters of the model, and would it be more suitable than conventional machine learning models for regions that are without or lacking data? [PERSON] et al. (2021) [PERSON] et al. (2021) applied an artificial neural network to a frozen soil hydrological model of small watersheds in the hinterland of the Qinghai-Tibet Plateau. After training, the model could simulate and predict the runoff in a small watershed in the frozen soil area of the Qinghai-Tibet Plateau by relying on precipitation and temperature as inputs. It provided a simple and effective method with a certain physical significance for the study of the runoff of small watersheds in a frozen soil area without observation data, such as soil temperature and moisture. [PERSON] et al. (2018) [PERSON] et al. (2018) applied the BP neural network algorithm to the estimation of water flux and found that the performance of the neural network model was significantly better than that of the machine learning model under the same conditions, which indicated that they have strong adaptability. [PERSON] (2015) [PERSON] (2015) further elucidated the deep learning algorithm, which differs from machine learning in that the traditional machine learning framework is feature extraction and classification, while the deep learning framework is feature learning and classification, and the latter has a stronger learning ability and generalization ability. In this study, the MLP model was found to be the most stable, with input characteristics ranging from three to eight variables, and the fitting accuracy fluctuated by about 1%. Considering that the MLP model was stable and optimal, the MLP deep neural network was used for NEE prediction. Additionally, the multilayer perceptron was based on the single layer neural network, which was composed of one or more hidden layers, and, thus, became the deep neural network. Its parameters were adjusted through supervised learning and forward and backward propagation, which accorded with the [PERSON] (2015) [PERSON] (2015) analytic deep learning framework. The network input dimension was set to have five parameters, referring to the five meteorological factors in Section 2.2.1, which had a relatively high correlation between the NEE and meteorological factors, including shortwave downward radiation (RG), saturated water pressure (VPD), air temperature (Tair), relative humidity (RH), and wind speed (WS). The learning rate of multilayer perceptron training was set to 0.001 through cross-validation and parameter tuning, and the batch size was set to 32. The training time epoch was set to 500, and the optimizer selected was Adam. Additionally, the hidden layer was set to 20,20,5. The output dimension was set to one, which was the NEE prediction value. The model decision coefficient R\({}^{2}\) obtained through the training of this network model was 0.58, which was larger than that of the previous MLP model (0.57). It showed that the precision of the deep learning model can be improved by using fewerinput parameters under the optimization of the network structure, and this compares with [PERSON] (2022) [41] and [PERSON] (2020) [42], who quantitatively evaluated and compared the multilayer perceptron of the algorithms with CART, KNN, NB, RF, and SVM by using accuracy and F1 (a comprehensive evaluation of precision and recall).