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

Soil temperature (TS) is a crucial parameter in many fields, especially agriculture. In developing countries like Algeria, the soil temperatures (TS) 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.