Hybrid Multi-Criteria Decision-Making (MCDM) Approaches with Random Forest Regression for Interval-Based Fuzzy Uncertainty Management

Faezeh Jokar ¹ ( Department of Science, Islamic Azad University, Isfahan( Khorasgan) Branch, Isfahan, Iran. )
Mohammad Jalali Varnamkhasti ² ( Department of Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. )
Abdollah Hadi-Vencheh ³ ( Department of Mathematics,Islamic Azad University, Isfahan (Khorasgan) Branch, Isfahan, Iran. )

تاریخ ارسال : 1403/09/25 تاریخ تایید : 1403/11/30

کلید واژه: fuzzy sets, hybrid methods, multi-Criteria decision making, uncertainty.,

چکیده مقاله :

This research presents an innovative framework that combines Hybrid Multi-Criteria Decision-Making (MCDM) approaches with Random Forest Regression to address interval-based fuzzy uncertainty in renewable energy project evaluation. Traditional Fuzzy TOPSIS methods often struggle with the inherent uncertainty and complexity of real-world data, which can lead to suboptimal decision-making. To enhance decision accuracy, we propose a hybrid solution that integrates Higher Interval TOPSIS with Random Forest Regression. This methodology effectively captures intricate interdependencies among project attributes—including cost, energy output, environmental impact, and social acceptance—within an interval-based fuzzy context. We applied our approach to a dataset of renewable energy projects and compared it against conventional Fuzzy TOPSIS methods. Results indicated significant improvements in predictive performance, achieving a Mean Absolute Error (MAE) of 0.045, a Mean Squared Error (MSE) of 0.0029, and an R² value of 0.95, highlighting the framework's ability to explain 95% of the variability in outcomes. This research underscores the promise of integrating AI-driven techniques within MCDM frameworks to enhance decision-making under uncertainty in the renewable energy sector.

چکیده انگلیسی :

This research presents an innovative framework that combines Hybrid Multi-Criteria Decision-Making (MCDM) approaches with Random Forest Regression to address interval-based fuzzy uncertainty in renewable energy project evaluation. Traditional Fuzzy TOPSIS methods often struggle with the inherent uncertainty and complexity of real-world data, which can lead to suboptimal decision-making. To enhance decision accuracy, we propose a hybrid solution that integrates Higher Interval TOPSIS with Random Forest Regression. This methodology effectively captures intricate interdependencies among project attributes—including cost, energy output, environmental impact, and social acceptance—within an interval-based fuzzy context. We applied our approach to a dataset of renewable energy projects and compared it against conventional Fuzzy TOPSIS methods. Results indicated significant improvements in predictive performance, achieving a Mean Absolute Error (MAE) of 0.045, a Mean Squared Error (MSE) of 0.0029, and an R² value of 0.95, highlighting the framework's ability to explain 95% of the variability in outcomes. This research underscores the promise of integrating AI-driven techniques within MCDM frameworks to enhance decision-making under uncertainty in the renewable energy sector.

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