# Optimization of Data-Driven Soil Temperature Forecast--The First Model in Bangladesh
## 1 Introduction  
Soil temperature influences the physical, chemical, and microbiological processes that take place in soil [1; 2]. It significantly influences the operational seasons of water systems and exerts a profound impact on evapotranspiration, plant growth, development, oxygen availability, and health and yield of various grain crops [3; 4; 5; 6]. A further increase in soil temperature in tropical regions would lead to more negative effects, including seedling death, smaller plants, higher water needs, and increased vulnerability to different plant diseases [7]. At the same time, since soil temperature records can be used to investigate the link between meteorological factors [8], soil temperature often plays a crucial role in numerical weather prediction and is a crucial indicator of climate change patterns [9; 10; 11; 12]. Therefore, monitoring and predicting soil temperature can greatly support the development of sustainable agriculture by providing insights into soil health for seed germination, fruiting, flowering, and the growth of crop species. Numerous scientific and technical fields have taken notice of the increasing use of machine learning [13; 14], largely because of their superior accuracy to conventional forecast techniques and their effectiveness in cutting down on processing time [15; 16]. The main advantage of machine learning models is that they are driven by unknown internal links embedded inside data, rather than necessarily taking into account complex physical mechanisms [17]. [PERSON] et al. [18] investigated the predictive accuracy and usability of machine learning models in hydrological inference and revealed that machine learning models have high prediction accuracy but relatively low interpretability of inner mechanisms. Although machine learning excels in accuracy and efficiency, it is often opaqueand lacks the transparency found in physics-based models. [PERSON] et al. [19] combined process-oriented, physics-based modeling with machine learning to enhance streamflow estimations and understand the inner mechanisms. [PERSON] [13] used a similar approach to improve existing groundwater numerical modeling. Compared with the process-oriented, physics-based model approach, machine learning can only provide limited insight into understanding soil dynamic changes, but due to the lack of observed data to drive process-oriented, physics-based models in poor countries, machine learning has become a more effective and low-cost approach compared to other models. The direct measurements of long-term soil temperature changes in economically challenged countries are expensive and difficult to obtain at present. The latest Internet of Things (IoT) sensors are being applied to environmental pollution [20; 21], water quality [22], and precision agriculture [23; 24; 25; 26; 27], so the large-scale implementation of IoT sensors is a potentially solution to improve future soil temperature observation in these economically challenged countries. Although different land surface models can simulate soil temperature well, these models need to be driven by many downscaled atmospheric forcing variables and soil parameters (e.g., soil thermal diffusive) [3]. These inputs are not available for developing countries, and the simulation process of physics-based models is time-consuming and expensive, leading to machine learning approaches playing a more important role in estimating soil temperature. [PERSON] [28] employed neural networks to forecast soil temperature in Wyoming, USA, by inputting three meteorological factors (air temperature, net radiation, and relative humidity) and two hydrological factors (precipitation and runoff). [PERSON] and [PERSON] [29] employed a similar approach to forecast the monthly average soil temperature at various depths in Turkey. [PERSON] et al. [30] revealed that thermal conductivity, short-term climatic conditions, and moisture content have a significant impact on soil temperature in Turkey. [PERSON] et al.