# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 50 cm Depth in Bangladesh
#### 5.3.1 One-Day Lead-Time Forecast
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Model** & **Time Window Size** & \multicolumn{2}{c}{**One**} & \multicolumn{2}{c}{**Two**} & \multicolumn{2}{c}{**Three**} & \multicolumn{2}{c}{**Four**} & \multicolumn{2}{c}{**Five**} \\ \hline \hline \multirow{10}{*}{Hybrid} & **Scenarios** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** \\ \cline{2-11}  & MEAN & 2.283 & 0.895 & 2.179 & 0.908 & 2.105 & 0.917 & 2.057 & 0.923 & 2.025 & 0.926 \\ \cline{2-11}  & MEAN-SUN & 2.246 & 0.901 & 2.152 & 0.912 & 2.089 & 0.920 & 2.056 & 0.925 & 2.022 & 0.929 \\ \cline{2-11}  & MEAN-MAX-MIN & 2.121 & 0.913 & 2.033 & 0.923 & 1.978 & 0.929 & 1.945 & 0.933 & 1.920 & 0.936 \\ \cline{2-11}  & MEAN-HUM-RAIN & 2.186 & 0.907 & 2.083 & 0.920 & 2.018 & 0.926 & 1.981 & 0.930 & 1.957 & 0.933 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 2.125 & 0.914 & 2.045 & 0.923 & 1.992 & 0.929 & 1.958 & 0.933 & 1.930 & 0.936 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 2.115 & 0.915 & 2.036 & 0.924 & 1.984 & 0.930 & 1.958 & 0.933 & 1.928 & 0.936 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-FIUM & 2.108 & 0.917 & 2.037 & 0.925 & 1.984 & 0.931 & 1.954 & 0.934 & 1.928 & 0.936 \\ \cline{2-11}  & MEAN-MAX-MIN-RUN-RUN-RAIN & 2.103 & 0.916 & 2.028 & 0.926 & 1.977 & 0.931 & 1.949 & 0.934 & 1.925 & 0.937 \\ \hline \hline \multirow{10}{*}{Hybrid} & MEAN & 2.281 & 0.895 & 2.171 & 0.910 & 2.091 & 0.919 & 2.039 & 0.925 & 2.004 & 0.929 \\ \cline{2-11}  & MEAN-SUN & 2.235 & 0.903 & 2.136 & 0.915 & 2.070 & 0.923 & 2.027 & 0.928 & 1.994 & 0.932 \\ \cline{2-11}  & MEAN-MAX-MIN & 2.108 & 0.915 & 2.017 & 0.926 & 1.954 & 0.932 & 1.917 & 0.937 & 1.889 & 0.940 \\ \cline{2-11}  & MEAN-HUM-RAIN & 2.176 & 0.909 & 2.060 & 0.923 & 1.988 & 0.930 & 1.946 & 0.934 & 1.916 & 0.938 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 2.112 & 0.916 & 2.023 & 0.926 & 1.963 & 0.932 & 1.926 & 0.936 & 1.898 & 0.940 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 2.101 & 0.917 & 2.013 & 0.927 & 1.956 & 0.934 & 1.923 & 0.937 & 1.894 & 0.940 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 2.093 & 0.919 & 2.010 & 0.928 & 1.955 & 0.934 & 1.920 & 0.938 & 1.893 & 0.941 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 2.084 & 0.919 & 2.000 & 0.929 & 1.946 & 0.935 & 1.914 & 0.938 & 1.888 & 0.942 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Conf. **Best combinations for one-day lead-time forecast**: In a one-day lead-time forecasting of ST50, the combination of hybrid RF-GBT, eighth input scenario, and five-day time window achieved the best forecast performance with the lowest average RMSE (1.888 \({}^{\circ}\)C) and the highest average R (0.942). For four-day, three-day, and two-day time windows, the best predictions were obtained with the hybrid RF-GBT, and a one-day time window with the RF, respectively. This underscored the superior forecast capability of the hybrid RF-GBT and RF for soil temperature at 50 cm depth. Figure 9 illustrates successful one-day advanced soil temperature forecasting for four stations, showing strong alignment between model predictions and observed values. The optimal model properly captured main soil temperature changes; moreover, it accurately forecasted extremely high soil temperatures at the Faridpur and Rajshahi stations.