| Summary: | Estimating soil temperature (ST) profile is identified as essential knowledge for plants, crop growth, and germination in all agriculture regions. In this study, daily soil temperature (DST) was modeled using Multilayer perceptron (MLP) model, Gaussian Process (GP), Random Forest (RF), and the M5P model methods for estimating and comparing DST in arid regions. The data selected to test the proposed models are obtained from two stations in Tabriz and Ahar, located in the Azerbaijan province of Iran. Input dataset includes air temperature, relative humidity, wind speed, and sunshine as dependent parameters, whereas ST at depths of 5 cm was selected for the target in model development. The results show the MLP works better than GP-, RF-, and M5P-based models in estimating the DST, with excellent performance indicators such as the mean absolute error, root mean square error, and coefficient of correlation. Results showed that the MLP model with RMSE = 3.2626°C was more suitable than other models in ST estimation 2 days ahead for Tabriz station. Also, in Ahar, MLP with RMSE = 6.3332°C was more suitable than GP-, RF-, and M5P-based models for estimating DST. As a conclusion, the developed MLP is recommended for estimating the DST profiles.
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