Extreme learning machine for soil temperature prediction using only air temperature as input

Abstract Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (ST) and the meteorological data are limited. This study investigates the use of Extreme Learning Machine (ELM) for the accurate prediction of daily ST at three different depths (30 cm, 60 cm, and 100 cm) using a minimal number of climatic inputs. The inputs used in this study include maximum and minimum air temperatures, relative humidity, and day of the year (DOY) as a representative of the temporal component. Five different combinations of inputs were used to develop ELM models and determine the best set of input variables. The ELM models were then compared with traditional methods such as multiple linear regression, artificial neural networks, and adaptive neuro-fuzzy inference system. Based on evaluation metrics such as R, RMSE, and MAPE, the ELM models with air temperatures and DOY as inputs (ELM-M0 and ELM-M3) demonstrated superior performance at all depths when compared to the other techniques. The most accurate predictions were found at a depth of 100 cm using the ELM-M3 model, which employed inputs of minimum and maximum air temperatures and DOY, with R value of 0.98, RMSE of 0.68°C, and MAPE of 3.4%. The results demonstrate that the inclusion of DOY in the climatic dataset significantly enhances the performance and accuracy of machine learning models for ST prediction. The ELM was found to be a fast, simple, effective, and useful tool for TS prediction.