# Assessment of Six Machine Learning Methods for Predicting Gross Primary Productivity in Grassland
## 4 Results
### Accuracy Assessment Results  
The accuracy assessment results of different machine learning models are shown in Table 3.  
For absolute error, most models had a mean absolute error (MAE) between 1 and 2. Of these, LightGBM and XGBoost performed best, with MAEs of 1.212 and 1.252, respectively, followed by RF, GBDT, and MLP, with MAEs of 1.344, 1.388, and 1.920, respectively. AdaBoost performed the worst, with an MAE of over 3, which is significantly higher than other models. For the degree of bias, most models had a root mean square error (RMSE) between 3 and 4. Of these, XGBoost and LightGBM performed best, with RMSEs of 3.148 and 3.149, respectively, followed by RF, GBDT, and MLP, with RMSEs of 3.309, 3.337, and 3.919, respectively. AdaBoost performed the worst, with an MRSE of more than 5, which is also significantly higher than other models. For model reliability, the relative percentage difference (RPD) of different models ranged from 3 to 6. Of these, XGBoost and LightGBM performed best, with RPDs of 5.922 and 5.920, respectively, followed by RF, GBDT, and MLP, with RPDs of 5.633, 5.629, and 4.837, respectively. AdaBoost performed the worst, with an RPD of only 3.499. Figure 4: Time series GPP prediction results of machine learning models on 9 samples. (**a–c**), forested grassland sampling points; (**d–f**), savanna grassland sampling points; (**g–i**), typical grassland sampling points, where (**i**) is the Xilingol Temperate Typical Grassland Flux Station. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **MAE** & **RMSE** & **RPD** & **R2** & **Bias** \\ \hline MLP & 1.920 & 3.919 & 4.837 & 0.956 & \(-\)0.492 \\ RF & 1.344 & 3.309 & 5.633 & 0.968 & \(-\)0.416 \\ AdaBoost & 3.067 & 5.307 & 3.499 & 0.918 & \(-\)0.047 \\ GBDT & 1.388 & 3.337 & 5.629 & 0.968 & \(-\)0.751 \\ XGBoost & 1.252 & 3.148 & 5.922 & 0.971 & \(-\)0.239 \\ LightGBM & 1.212 & 3.149 & 5.920 & 0.971 & \(-\)0.034 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The accuracy assessment results of machine learning models in GPP prediction. For the degree of fit, the regression determination coefficient (R2) of the different models was all above 0.9, indicating that the prediction results fit well with the reference data. Among them, XGBoost and LightGBM performed best, with an R2 of 0.971, followed by RF and GBDT, with R2s reaching 0.968. MLP and AdaBoost performed poorly, with R2s of 0.956 and 0.918, respectively. For the degree of overestimation or underestimation, the mean bias (bias) of different models was between \(-0.8\) and 0, indicating that each model has a different degree of underestimation tendency. Among them, LightGBM and AdaBoost were the least underestimated, with biases of \(-0.034\) and \(-0.047\), respectively. XGBoost, RF, and MLP followed, with biases of \(-0.239\), \(-0.416\), and \(-0.492\), respectively. GBDT performed the worst, with a bias of \(-0.751\). Combining the accuracy assessment results of the five aspects, LightGBM and XGBoost have the best overall performance in GPP prediction. Among them, the absolute error of LightGBM prediction results was smaller; XGBoost is better in the degree of result bias and model reliability. The overall performance of the AdaBoost model is the worst and is quite different from the other models. Further, we fit the prediction data from different models to the reference data on the Mongolian Plateau scale, and the results are shown in Figure 5. In the different models, most of the points are distributed near the reference oblique line; R2 is higher than 0.9, and the fitting effect is good. This shows that the predicted data are very close to the reference data, demonstrating the excellent performance of the machine learning models. A detailed analysis of each model shows that the distribution patterns of the points in Figure 5a,b,d,e are very similar: there are more points at the bottom right of the reference line (predicted data equal to reference data). This shows that the predicted value of GPP output by MLP, RF, GBDT, and XGBoost is low.