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.

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the Firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013–2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5_cm) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST_5cm and ST _20cm of Tabriz station and ST_10cm of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.

Studying the status of agricultural soils is one of the most important concerns in the agricultural sector. The soil organic carbon (SOC) is one of the main parameters and it plays an important role in improving soil properties. Hence, knowing this parameter is important in soil science. This study applied the pattern recognition (PR) method in predicting the SOC. Also, the ability of this method was compared with different methods such as the Radial Basis Function Network (RBF), Multilayer Perceptron Neural Network (MLP), Multiple Linear Regression (MLR) and Support Vector Regression (SVR). To compare the results, four performance criteria, namely, root mean square errors (RMSE), the Nash–Sutcliffe efficiency (NS), Willmott’s Index of agreement (WI), mean absolute error (MAE) and Taylor diagrams were used. Results indicated that the PR model performed significantly better than the MLP, MLR, SVR, and RBF models for the estimation of the SOC.