Abstract
Soil temperatures at both surface and various depths are important in changing environments to understand the biological, chemical, and physical properties of soil. This is essential in reaching food sustainability. However, most of the developing regions across the globe face difficulty in establishing solid data measurements and records due to poor instrumentation and many other unavoidable reasons such as natural disasters like droughts, floods, and cyclones. Therefore, an accurate prediction model would fix these difficulties. Uzbekistan is one of the countries that is concerned about climate change due to its arid climate. Therefore, for the first time, this research presents an integrated model to predict soil temperature levels at the surface and 10 cm depth based on climatic factors in Nukus, Uzbekistan. Eight machine learning models were trained in order to understand the best-performing model based on widely used performance indicators. Long Short-Term Memory (LSTM) model performed in accurate predictions of soil temperature levels at 10 cm depth. More importantly, the models developed here can predict temperature levels at 10 cm depth with the measured climatic data and predicted surface soil temperature levels. The model can predict soil temperature at 10 cm depth without any ground soil temperature measurements. The developed model can be effectively used in planning applications in reaching sustainability in food production in arid areas like Nukus, Uzbekistan.