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

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. HIGHLIGHTS Modelling soil temperature at different depths based on meteorological variables using vote algorithm.; Forecasting soil temperature at 1, 2, and 3 days ahead at depths of 5 and 50 cm using machine learning models.; M5P, RF, and RT are applied.; An ensemble approach with the vote algorithm (i.e. V-M5P, V-RF, and V-RT) is also proposed.;

Keywords

• forecasting
• m5prime
• random forest
• random tree
• soil temperature
• vote