A Multi-Criteria Decision Support Framework for Agricultural Fuel Demand Forecasting: Comparative Analysis of Machine Learning Approaches with Domain-Specific Feature Engineering

Abstract

This study presents a comprehensive multi-criteria decision support framework for agricultural fuel demand prediction using machine learning algorithms. The research utilizes real operational data from Tarnet A.S., comprising 21,187 daily transaction records collected from agricultural cooperatives in Antalya, Türkiye, spanning from 2019 to 2021. A novel Agricultural Fuel Demand Feature Taxonomy (AFDFT) is proposed, organizing 844 features into five hierarchical categories: temporal, agricultural activity, operational, environmental, and economic factors. Eight machine learning algorithms were systematically evaluated using a multi-criteria approach that considers accuracy, statistical significance, effect size, and performance stability. The XGBoost algorithm emerged as the optimal solution, achieving 78.01% classification accuracy with statistically significant superiority (Friedman test χ²(7)= 23.15, P<0.01) and small to large effect sizes (Cohen's d ranging from 0.42 to 12.84). The framework demonstrates practical applicability for decision support in agricultural fuel management, providing actionable insights for resource optimization in the agricultural sector.

Keywords

• Agricultural fuel consumption
• Machine learning
• Decision support systems
• XGBoost
• Feature taxonomy

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