Enhanced forecasting of multi-step ahead daily soil temperature using advanced hybrid vote algorithm-based tree models

Abstract

Abstract In this study, the vote algorithm used to improve the performances of three machine-learning models including M5Prime (M5P), random forest (RF), and random tree (RT) is developed (i.e. V-M5P, V-RF, and V-RT). Developed models were tested for forecasting soil temperature (TS) at 1, 2, and 3 days ahead at depths of 5 and 50 cm. All models were developed using different climatic variables, including mean, minimum, and maximum air temperatures; sunshine hours; evaporation; and solar radiation, which were evaluated. Correlation coefficients of 0.95 for the V-M5P model, 0.95 for the V-RF model, and 0.91 for the V-RT model were recorded for both 1- and 2-day ahead forecasting at a depth of 5 cm. For 3-day ahead forecasting, V-RF was the superior model with Nash–Sutcliff efficiency (NSE) values of 0.85, compared to V-M5P's value of 0.81 and V-RT's value of 0.81. The results at a depth of 5 cm indicate that V-RT was the least effective model. At a depth of 50 cm, forecasted TsS was in good agreement with measurements, and the V-RF was slightly superior. Among the limitations of the current work is that the models were unable to improve their performances by increasing the forecasting horizon.