# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 6 Conclusions  
Banglades faces significant challenges in adapting to the impact of climate change and is often cited as one of the most vulnerable countries to climate change ([32; 33]). Since soil temperature complexities have far-reaching effects on agriculture, influencing anything from root development to microbial activity, change patterns of soil temperature are of great importance for an agro-based economy like Bangladesh. Soil properties, such as texture, moisture content, and organic matter, work together to determine the flow of heat through the Earth, influencing temperature changes. The process-oriented, physics-based models incorporate these soil properties into the forecasting process. Compared with this modeling approach, machine learning can only provide limited insight into understanding soil dynamic changes. However, due to the lack of enough observation datasets to drive process-oriented, physics-based models in poor countries like Bangladesh, machine learning has become a more effective and low-cost quantitative approach. Choosing the optimal model for soil temperature forecasting is not one-size-fits-all. More input variables and more complex models cannot guarantee a high forecast performance. It must involve a careful evaluation of the specific requirements, resources, and constraints of the application to make an informed decision about which model is best suited for the task. In this study, we explored the combination of five tree-based models, eight input scenarios, and five sizes of time windows to forecast soil temperature at 10 cm/30 cm/50 cm depth under three types of lead times. The five tree-based models, including DT, GBT, RF, hybrid DTGBT, and hybrid RF-GBT, were evaluated for one-day, three-day, and five-day lead-time forecasts in terms of both RMSE and R values. The optimal combination to produce the best soil temperature forecast and related accuracy are shown in Table 6. The hybrid RF-GBT model with the eighth input scenario and five-day time window is optimal for forecasting soil temperature at the depth of 10 cm and 30 cm, whereas the RF model with the eighth input scenario and five-day time window is optimal for forecasting soil temperature at the depth of 50 cm when the lead time is long (five days). The best accuracy of soil temperature at depths of 10 cm, 30 cm, and 50 cm were RMSE:1.752 \({}^{\circ}\)C and R:0.945; RMSE: 1.188 \({}^{\circ}\)C and R: 0.962; RMSE: 1.888 \({}^{\circ}\)C and R: 0.942, respectively. Since our study includes the first soil temperature forecast model from meteorological observations in Bangladesh, it will save the soil observation cost significantly, fill in missing soil temperature observations, and enhance the accessibility and accuracy of soil temperature data, making it especially valuable for research and informed decision-making in Bangladesh. At the same time, it provides valuable insights for agricultural soil manage \begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil Depth** & **Lead Time** & **Input Scenario + Time Window Size + Model** & **RMSE (\({}^{\circ}\)C)** & **R** \\ \hline \multirow{3}{*}{10 cm} & One-day & 8 th & Five-day & Hybrid RF-GBT & 1.973 & 0.945 \\ \cline{2-6}  & Three-day & 8 th & Five-day & Hybrid RF-GBT & 1.752 & 0.927 \\ \cline{2-6}  & Five-day & 8 th & Four-day & Hybrid RF-GBT & 1.908 & 0.911 \\ \hline \multirow{3}{*}{30 cm} & One-day & 8 th & Five-day & Hybrid RF-GBT & 1.188 & 0.962 \\ \cline{2-6}  & Three-day & 8 th & Five-day & Hybrid RF-GBT & 1.260 & 0.956 \\ \cline{2-6}  & Five-day & 8 th & Five-day & Hybrid RF-GBT & 1.398 & 0.945 \\ \hline \multirow{3}{*}{50 cm} & One-day & 8 th & Five-day & Hybrid RF-GBT & 1.888 & 0.942 \\ \cline{2-6}  & Three-day & 3 rd & Five-day & Hybrid RF-GBT & 1.901 & 0.939 \\ \cline{2-6}  & 8 th & Five-day & Hybrid RF-GBT & 1.905 & 0.940 \\ \cline{2-6}  & Five-day & 3 rd & Five-day & RF & 1.962 & 0.933 \\ \cline{2-6}  & 8 th & Five-day & RF & 1.971 & 0.935 \\ \hline \end{tabular}
\end{table} 
Table 6: Best model configures for soil temperature forecast in Bangladesh.