# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 5 Discussion  
In this study, six machine learning models (MLP, RF, AdaBoost, GBDT, XGBoost, and LightGBM) were used to predict the GPP of the Mongolian Plateau, all of which achieved good results. The change tendency of the predicted data and the reference data are in good agreement. Through the detailed evaluation and comprehensive trade-off of five accuracy indicators (MAE, RMSE, RPD, R\({}^{2}\), and bias), we found that LightGBM has the  
Figure 6: The number of times the impact factors were determining factors (**a**) and their contribution ratio to accuracy (**b**). best overall performance. Its absolute error is small (MAE is less than 1.3), the degree of deviation is low (RMSE is less than 3.2), the model reliability is strong (RPD is greater than 5.9), and the degree of fit with the reference data is high (R\({}^{2}\) is greater than 0.97). It has the smallest degree of underestimation, and the predicted data is closest to the reference data (bias is only \(-\)0.034). LightGBM uses a unilateral gradient algorithm and a leaf-wise strategy to generate a decision tree [61; 62]. This model helps to better focus on important samples and mine feature relationships, making the algorithm more accurate and efficient when processing large amounts of data. In addition, the loss function of LightGBM also uses the second-order Taylor expansion and regularization method, which can effectively prevent overfitting and improve prediction accuracy [63; 64]. In contrast, other machine learning models such as AdaBoost, GBDT, and XGBoost may be deficient in some aspects. Their loss functions are relatively simple and therefore have some limitations when applied to complex prediction tasks [65]. In addition, some models such as RF also have some shortcomings, for example, they cannot effectively remove the noise in the input data and are prone to overfitting problems [66]. However, the structure of the MLP model is relatively simple and it is difficult to learn complex non-linear relationships, so its performance in GPP prediction is relatively weak [67; 68]. Based on the results of the comprehensive accuracy assessment and characteristics analysis, it can be considered that LightGBM has higher practicability and reliability in GPP prediction and is suitable for a wide range of grassland GPP prediction tasks. In future studies, we will try different aggregation strategies, consider introducing emerging new algorithms, and compare their results to provide more and better methods for GPP prediction. Selecting the appropriate impact factors is the key to grasping the driving and response rule between GPP and impact factors and is also the basis of accurately predicting GPP. This study refers to the excellent experience of the existing research [35; 41; 42], tries to include all the influential factors as much as possible, and then explores the most important part in the prediction of GPP. Among the 14 impact factors selected, there is inevitably a correlation. For example, NDVI can better reflect the change in vegetation greenness. EVI is an improvement over NDVI and is superior to NDVI in reducing background and atmospheric effects and saturation problems [69; 70]. Both have similar uses, and there is a strong correlation [71; 72]. However, this study points out that EVI and NDVI are both key factors in GPP prediction, and both have a significant impact on the improvement of prediction accuracy. In other words, even if the overlap information in them is removed, the influence of the remaining part may still be greater than that of other factors (such as LSWI and LAI). Further, the key and practical question we pay attention to is which impact factors can make the final prediction accuracy reach the expectation. In model training and GPP prediction, EVI, NDVI, P, LULC, Tmax, PET, and ET were the most frequently used determinants. Through recursive feature elimination analysis, we found that their contribution to GPP prediction accuracy was significantly higher than other factors, and the cumulative contribution ratio exceeded 95%.