Modeling Soil Temperature with Fuzzy Logic and Supervised Learning Methods

Abstract

Soil temperature is a critical environmental factor that affects plant development, physiological processes, and overall productivity. This study compares two modeling approaches for predicting soil temperature at various depths: (i) fuzzy logic-based systems, including the Mamdani fuzzy inference system (MFIS) and the adaptive neuro-fuzzy inference system (ANFIS); (ii) supervised machine learning algorithms, such as multilayer perceptron (MLP), support vector regression (SVR), random forest (RF), extreme gradient boosting (XGB), and k-nearest neighbors (KNN), along with multiple Linear regression (MLR) as a statistical benchmark. Soil temperature data were collected from Tokat, T & uuml;rkiye, between 2016 and 2024 at depths of 5, 10, 20, 50, and 100 cm. The dataset was split into training (2016-2021) and testing (2022-2024) periods. Performance was evaluated using the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R2). The ANFIS achieved the best prediction accuracy (MAE = 1.46 degrees C, RMSE = 1.89 degrees C, R2 = 0.95), followed by RF, XGB, MLP, KNN, SVR, MLR, and MFIS. This study underscores the potential of integrating machine learning and fuzzy logic techniques for more accurate soil temperature modeling, contributing to precision agriculture and better resource management.