# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 5 Results and Discussion
### Forecasting Soil Temperature at 30 cm Depth in Bangladesh
#### 5.2.2 Three-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 & 1.625 & 0.926 & 1.494 & 0.938 & 1.418 & 0.945 & 1.340 & 0.952 & 1.353 & 0.950 \\ \cline{2-11}  & MEAN-SUN & 1.580 & 0.931 & 1.453 & 0.942 & 1.386 & 0.948 & 1.324 & 0.953 & 1.323 & 0.952 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.453 & 0.941 & 1.357 & 0.949 & 1.302 & 0.954 & 1.247 & 0.958 & 1.253 & 0.957 \\ \cline{2-11}  & MEAN-HUM-RAIN & 1.528 & 0.934 & 1.396 & 0.946 & 1.338 & 0.951 & 1.282 & 0.955 & 1.284 & 0.955 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.446 & 0.941 & 1.351 & 0.949 & 1.298 & 0.953 & 1.241 & 0.957 & 1.244 & 0.957 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.431 & 0.942 & 1.341 & 0.95 & 1.288 & 0.954 & 1.237 & 0.958 & 1.239 & 0.958 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.427 & 0.942 & 1.334 & 0.950 & 1.280 & 0.954 & 1.229 & 0.958 & 1.230 & 0.958 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.415 & 0.943 & 1.323 & 0.951 & 1.273 & 0.955 & 1.223 & 0.959 & 1.227 & 0.958 \\ \hline \multirow{10}{*}{Hybrid} & MEAN & 1.624 & 0.926 & 1.481 & 0.939 & 1.401 & 0.947 & 1.388 & 0.95 & 1.326 & 0.953 \\ \cline{2-11}  & MEAN-SUN & 1.571 & 0.932 & 1.434 & 0.944 & 1.361 & 0.95 & 1.324 & 0.953 & 1.293 & 0.955 \\ \cline{1-1} \cline{2-11}  & MEAN-MAX-MIN & 1.442 & 0.941 & 1.335 & 0.951 & 1.276 & 0.956 & 1.241 & 0.958 & 1.219 & 0.960 \\ \cline{1-1} \cline{2-11}  & MEAN-HUM-RAIN & 1.513 & 0.935 & 1.369 & 0.948 & 1.302 & 0.954 & 1.264 & 0.957 & 1.242 & 0.959 \\ \cline{1-1} \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.436 & 0.942 & 1.328 & 0.951 & 1.269 & 0.955 & 1.234 & 0.958 & 1.210 & 0.960 \\ \cline{1-1} \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.419 & 0.943 & 1.315 & 0.952 & 1.257 & 0.956 & 1.227 & 0.959 & 1.203 & 0.961 \\ \cline{1-1} \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.411 & 0.944 & 1.306 & 0.952 & 1.248 & 0.957 & 1.216 & 0.959 & 1.192 & 0.961 \\ \cline{1-1} \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.397 & 0.945 & 1.295 & 0.953 & 1.241 & 0.957 & 1.210 & 0.960 & 1.188 & 0.962 \\ \hline \end{tabular}
\end{table} 
Table 4: Conf. **Best combination for three-day lead-time forecast:** The hybrid models, especially RF-GBT, stood out for their strong performance, particularly with larger time windows and eighth input scenarios. It achieved the lowest average RMSE (1.260 \({}^{\circ}\)C) and the highest average R value (0.956). Figure 7 demonstrates a strong agreement between optimal predictions by RF-GBT and actual observations at four soil temperature observation stations. Such a visual comparison confirmed that the RF-GBT hybrid model is highly capable of providing accurate and reliable predictions.