# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 10 cm Depth in Bangladesh
#### 5.1.1 One-Day Lead-Time Forecast  
In the context of one-day lead-time forecasting of soil temperature at 10 cm depth, Tables 1 and 2 evaluate tree-based model performance across various scenarios, emphasizing accuracy based on low errors and strong correlations between forecast and actual values. This provided some insights into understanding the complex relationships between meteorological factors and soil temperature fluctuations. **Forecast by DT:** In terms of average RMSE and R values, from Tables 1 and 2, it is clear that the forecast accuracy of the testing datasets is a little worse than that of the training datasets. For the testing dataset, the DT model with the third input scenario achieved the best forecast, consistently showing the lowest average RMSE values (ranging from 2.086 \({}^{\circ}\)C to 2.126 \({}^{\circ}\)C) and the highest average R values (ranging from 0.932 to 0.936) across various time window sizes. The smallest time window and the simplest input scenario exhibited the lowest average R and highest average RMSE values. As the size of the time window increased, the forecast accuracy of the model increased. This means that the DT model is sensitive to the input parameters and time window sizes. **Forecast by RF:** The forecast performance of the RF model with different input scenarios and different sizes of time window for the training dataset and testing datasets is demonstrated in Tables 1 and 2, respectively. It is clear that the forecast accuracy of the testing datasets is a little worse than that of the training datasets. For the testing dataset, the eighth input scenario, which combined all meteorological factors, yielded the best forecast accuracy when the size of the time window was one day, two days, three days, or five days, whereas the seventh input scenario performed best when the size of the time window was four days. Compared with DT, the RF achieved a slight improvement in the RMSE and R values. The lowest average RMSE value (1.994 \({}^{\circ}\)C) was achieved with the combination of the eighth input scenario and the largest time window. **Forecast by GBT:** The forecasting accuracy of the GBT model was consistent between the testing and training datasets, with the same optimal time window and input scenarios. For the testing dataset, the forecast by GBT showed a consistent trend where the average RMSE values increased with larger time windows, although the forecast performance varied with different input scenarios. The best GBT model utilizing all meteorological parameters (eighth scenario) and the largest time window achieved the lowest average RMSE (1.976 \({}^{\circ}\)C) and the highest average R value (0.945). When the size of the time window was reduced from five days to one day, the average RMSE value increased from 1.976 \({}^{\circ}\)C to 2.067 \({}^{\circ}\)C, and the average R value decreased from 0.945 to 0.937. **Forecast by Hybrid DT-GBT:** The forecast accuracy of the training data was slightly higher than that of the testing dataset. For the testing dataset, the combination of all meteorological factors as the model input consistently led to the lowest average RMSE value and the highest average R value. The hybrid structure excels in capturing complex relationships, especially achieving the lowest average RMSE of 1.994 \({}^{\circ}\)C when the size of the time window was set to three days, and the highest average R value (0.944) when the size of the time window was set to five days. **Forecast by Hybrid RF-GBT:** The results of the hybrid RF-GBT's forecast performance in terms of the average RMSE and R values from all soil temperature observation stations are shown in Tables 1 and 2. For the hybrid model, the prediction results were consistent for the testing and training datasets. The differences in terms of R and RMSE values indicated that the model's performance was more robust in training processing.