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

Yıl 2026, Cilt: 9 Sayı: 2, 692 - 701, 15.03.2026

Mustafa Çoban , Hüseyin Oktay Altun , Semih Yumusak

https://doi.org/10.34248/bsengineering.1861328

https://izlik.org/JA67LF28YK

Öz

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.

Anahtar Kelimeler

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

Etik Beyan

Ethics committee approval was not required for this study because there was no study on animals or humans.

Teşekkür

This work is derived from the master's thesis entitled "Prediction of Daily Fuel Needs in Agriculture with Machine Learning" by Mustafa Çoban (KTO Karatay University, 2021), incorporating and extending its data and findings through advanced analysis. The authors would like to thank Tarnet A.Ş. for providing the agricultural fuel consumption data used in this study. The authors also acknowledge the use of Claude (Anthropic) for assistance with code refactoring of the analysis scripts and proofreading during the preparation of this manuscript.

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