# 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
For the testing dataset, the eighth input scenario led to the best forecast under all time window sizes, achieving the best prediction with the lowest average RMSE (1.973 \({}^{\circ}\)C) and the highest average R (0.945) when the time window size was set to five. \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  & **Scenarios** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** & **RMSE** & **R** \\ \hline  & MEAN & 1.620 & 0.936 & 1.552 & 0.941 & 1.520 & 0.944 & 1.502 & 0.945 & 1.490 & 0.946 \\ \cline{2-11}  & MEAN-SUN & 1.538 & 0.943 & 1.506 & 0.945 & 1.484 & 0.946 & 1.474 & 0.947 & 1.465 & 0.948 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.479 & 0.947 & 1.452 & 0.949 & 1.436 & 0.950 & 1.425 & 0.950 & 1.416 & 0.951 \\ \cline{2-11}  & MEAN-HMM-RAN & 1.409 & 0.946 & 1.466 & 0.948 & 1.451 & 0.949 & 1.443 & 0.949 & 1.436 & 0.949 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.471 & 0.947 & 1.448 & 0.949 & 1.433 & 0.950 & 1.422 & 0.951 & 1.414 & 0.951 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.461 & 0.948 & 1.442 & 0.949 & 1.429 & 0.950 & 1.418 & 0.951 & 1.412 & 0.951 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.459 & 0.948 & 1.441 & 0.949 & 1.427 & 0.950 & 1.416 & 0.951 & 1.410 & 0.951 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.454 & 0.949 & 1.439 & 0.950 & 1.425 & 0.950 & 1.415 & 0.951 & 1.409 & 0.952 \\ \hline  & MEAN & 1.616 & 0.937 & 1.524 & 0.943 & 1.479 & 0.946 & 1.450 & 0.948 & 1.429 & 0.950 \\ \cline{2-11}  & MEAN-SUM & 1.509 & 0.945 & 1.446 & 0.949 & 1.414 & 0.951 & 1.393 & 0.952 & 1.379 & 0.953 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.440 & 0.949 & 1.330 & 0.953 & 1.361 & 0.954 & 1.342 & 0.956 & 1.329 & 0.956 \\ \cline{2-11}  & MEAN-HMM-RAN & 1.447 & 0.949 & 1.393 & 0.952 & 1.367 & 0.954 & 1.350 & 0.955 & 1.339 & 0.956 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.426 & 0.951 & 1.381 & 0.953 & 1.353 & 0.955 & 1.335 & 0.956 & 1.323 & 0.957 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.413 & 0.951 & 1.373 & 0.954 & 1.347 & 0.955 & 1.330 & 0.956 & 1.318 & 0.957 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.404 & 0.952 & 1.365 & 0.954 & 1.340 & 0.956 & 1.323 & 0.957 & 1.312 & 0.958 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.397 & 0.952 & 1.362 & 0.955 & 1.338 & 0.956 & 1.322 & 0.957 & 1.311 & 0.958 \\ \hline  & MEAN & 1.609 & 0.937 & 1.509 & 0.944 & 1.456 & 0.948 & 1.422 & 0.950 & 1.396 & 0.952 \\ \cline{2-11}  & MEAN-SUM & 1.489 & 0.946 & 1.407 & 0.952 & 1.360 & 0.955 & 1.330 & 0.956 & 1.306 & 0.958 \\ \cline{2-11}  & MEAN-MAX-MIN & 1.407 & 0.952 & 1.340 & 0.956 & 1.302 & 0.958 & 1.274 & 0.960 & 1.255 & 0.961 \\ \cline{2-11}  & MEAN-HMM-RAN & 1.418 & 0.951 & 1.340 & 0.956 & 1.300 & 0.958 & 1.272 & 0.960 & 1.254 & 0.961 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN & 1.386 & 0.953 & 1.320 & 0.957 & 1.282 & 0.960 & 1.255 & 0.961 & 1.236 & 0.962 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-RAIN & 1.367 & 0.954 & 1.308 & 0.958 & 1.272 & 0.960 & 1.245 & 0.962 & 1.227 & 0.963 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM & 1.350 & 0.955 & 1.288 & 0.959 & 1.253 & 0.961 & 1.226 & 0.963 & 1.208 & 0.964 \\ \cline{2-11}  & MEAN-MAX-MIN-SUN-HUM-RAIN & 1.337 & 0.956 & 1.281 & 0.96 & 1.245 & 0.962 & 1.219 & 0.963 & 1.203 & 0.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average accuracy of ST10 forecast by tree-based models for the training dataset.