Supervised Machine Learning Models for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

Abstract

In the work possibilities of applying computational intelligence, namely machine learning models, in the grain crops delivery from agricultural enterprises to the elevator are analyzed. The expediency of using regression models of machine learning to forecast fuel consumption by vehicles during the grain crops delivery is established. Based on the historical data of the enterprise on the orders execution for the grain crops delivery, which include key factors influencing fuel consumption, the article forecasts fuel consumption by vehicles using such models: Generalized Linear Model, Neural Network Model, Decision Tree Model and Random Forest Model. The developed models were evaluated according to efficiency criteria, including mean absolute error, root mean square error, mean absolute percentage error, total time and training time. According to the modelling results, it is found that the most accurate and relatively fast forecast of fuel consumption by vehicles is obtained by applying the Random Forest model with MAPE 7.8 %.