Abstract: The aim of this study is to develop an innovative, multi-dimensional, and uncertain decision-making model that can identify the most appropriate alternative irrigation method for the efficient use of water resources in agriculture. In this context, the proposed model is based on the integrated use of spherical fuzzy sets, machine learning, MEREC, and WASPAS methods. The evaluations obtained from ten experts were converted into spherical fuzzy numbers, and the experts' importance weights were objectively calculated using machine learning. Criteria weights were determined using the MEREC method, and alternatives were ranked using the WASPAS method. This hybrid approach both reduces expert subjectivity and objectively reflects the relationships between criteria. According to the findings, feasibility/technological suitability (0.152) emerged as the most important criterion, followed by environmental impacts (0.144). Among the alternatives, drip irrigation (2.226) was identified as the most suitable option for efficient use of water resources. This result demonstrates that modern, technology-based irrigation systems should be a priority in sustainable agricultural policies. This study's contribution to the literature is its ability to bring objectivity, transparency, and the ability to manage high uncertainty to decision-making processes in agricultural water management. The model offers both methodological innovation and a practical decision-support tool at the application level.

Edanur Ergün, Serkan Eti, Serhat Yüksel and Hasan Dinçer. “A Hybrid Spherical Fuzzy–Machine Learning Model for Multi-Criteria Decision-Making in Sustainable Water Resource Management”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161264

@article{Ergün2025,
title = {A Hybrid Spherical Fuzzy–Machine Learning Model for Multi-Criteria Decision-Making in Sustainable Water Resource Management},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161264},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161264},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Edanur Ergün and Serkan Eti and Serhat Yüksel and Hasan Dinçer}
}