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

الموضوعات : فصلنامه ریاضی

Faezeh Jokar ¹ , Mohammad Jalali Varnamkhasti ² , Abdollah Hadi-Vencheh ³

1 - Department of Science, Islamic Azad University, Isfahan( Khorasgan) Branch, Isfahan, Iran.
2 - Department of Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
3 - Department of Mathematics,Islamic Azad University, Isfahan (Khorasgan) Branch, Isfahan, Iran.

تاريخ الإرسال : 14 الأحد , جمادى الثانية, 1446 تاريخ التأكيد : 19 الثلاثاء , شعبان, 1446 تاريخ الإصدار : 29 الجمعة , شعبان, 1446

الکلمات المفتاحية: fuzzy sets, hybrid methods, multi-Criteria decision making, uncertainty.,

ملخص المقالة :

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.

المصادر:

[1] A. Alone, A. K. Shukla, G. Nandan and D. R. Pattanaik, Predicting temperature variability in major Indian cities using Random Forest Regression (RFR) Model. Journal of Earth System Science, 134(1) (2025) 38.
[2] B. Babar, L. T. Luppino, T. Boström and S. N. Anfinsen, Random forest regression for improved mapping of solar irradiance at high latitudes. Solar Energy, 198 (2020) 81-92.
[3] T. Baležentis and S. Zeng, Group multi-criteria decision making based upon interval-valued fuzzy numbers: an extension of the MULTIMOORA method. Expert Systems with Applications, 40(2) (2013) 543-550.
[4] A. L. Balogun, A. Tella, Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression. Chemosphere, 299 (2022) 134250.
[5] D. E. Brashers, Communication and uncertainty management. Journal of communication, 51(3) (2001) 477-497.
[6] P. P. Bonissone, R. Subbu and J. Lizzi, Multicriteria decision making (MCDM): a framework for research and applications. IEEE Computational Intelligence Magazine, 4(3) (2009) 48-61.
[7] O. Castillo, L. Amador-Angulo, J. R. Castro and M. Garcia-Valdez, A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Information Sciences, 354 (2016) 257-274.
[8] E. Celik, M. Gul, N. Aydin, A. T. Gumus, A. F. Guneri, A comprehensive review of multi criteria decision making approaches based on interval type-2 fuzzy sets. Knowledge-Based Systems, 85 (2015) 329-341.
[9] K. P. Chiao, Multi-criteria decision making with interval type 2 fuzzy Bonferroni mean. Expert systems with applications, 176 (2021) 114789.
[10] L. A. de Vasconcelos Gomes, M. G. dos Santos and A. L. F. Facin, Uncertainty management in global innovation ecosystems. Technological Forecasting and Social Change, 182 (2022) 121787.
[11] E. de Rocquigny, N. Devictor and S. Tarantola, (Eds.) Uncertainty in industrial practice: a guide to quantitative uncertainty management, (2008) John Wiley & Sons.
[12] R. Ghasempour, M. A. Nazari, M. Ebrahimi, M. H. Ahmadi and H. Hadiyanto, Multi-Criteria Decision Making (MCDM) Approach for Selecting Solar Plants Site and Technology: A Review. International Journal of Renewable Energy Development, 8(1) (2019).
[13] G. Grote, Uncertainty management at the core of system design. Annual reviews in control, 28(2) (2004) 267-274.
[14] J. Guo, X. Zan, L. Wang, L. Lei, C. Ou and S. Bai, A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys. Engineering Fracture Mechanics, 293 (2023) 109714.
[15] S. Han, and H. Kim, Optimal feature set size in random forest regression. Applied Sciences, 11(8) (2021) 3428.
[16] Z. El Mrabet, N. Sugunaraj, P. Ranganathan and S. Abhyankar, Random forest regressor-based approach for detecting fault location and duration in power systems. Sensors, 22(2) (2022) 458.
[17] J. Hu, Y. Zhang, X. Chen, Y. Liu, Multi-criteria decision making method based on possibility degree of interval type-2 fuzzy number. Knowledge-Based Systems, 43 (2013) 21-29.
[18] R. Imran, K. Ullah, Z. Ali and M. Akram, A multi-criteria group decision-making approach for robot selection using interval-valued intuitionistic fuzzy information and Aczel-Alsina Bonferroni means. Spectrum of Decision Making and Applications, 1(1) (2024) 1-32.
[19] N. Iriani, A. Agustianti, R. Sucianti, A. Rahman and W. Putera, Understanding Risk and Uncertainty Management: A Qualitative Inquiry into Developing Business Strategies Amidst Global Economic Shifts, Government Policies, and Market Volatility. Golden Ratio of Finance Management, 4(2) (2024) 62-77.
[20] C. Kahraman, N. Yasin Ateş, S. Çevik, M. Gülbay and S. Ayça Erdoğan, Hierarchical fuzzy TOPSIS model for selection among logistics information technologies. Journal of Enterprise Information Management, 20(2) (2007) 143-168.
[21] M. W. Kramer, Uncertainty management. In Global encyclopedia of public administration, public policy, and governance (2023) 12961-12967, Cham: Springer International Publishing.
[22] A. Kumar, B. Sah, A. R. Singh, Y. Deng, X. He, P. Kumar and R. C. Bansal, A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and sustainable energy reviews, 69 (2017) 596-609.
[23] M. Lak Kamari, H. Isvand and M. Alhuyi Nazari, Applications of multi-criteria decision-making (MCDM) methods in renewable energy development: A review. Renewable Energy Research and Applications, 1(1) (2020) 47-54.
[24] Q. Liang and J. M. Mendel, Interval type-2 fuzzy logic systems: theory and design. IEEE Transactions on Fuzzy systems, 8(5) (2000) 535-550.
[25] H. Liao, J. Wang, M. Tang and A. Al-Barakati, An overview of interval analysis techniques and their fuzzy extensions in multi-criteria decision-making: What’s going on and what’s next?. International Journal of Fuzzy Systems, 25(5) (2023) 2081-2108.
[26] H. Li, X. Dai, L. Zhou andW. Yang, Multi-criteria constrained interval type-2 fuzzy decision-making: A space analysis perspective. Information Sciences, 669 (2024) 120581.
[27] Y. Li, C. Zou, M. Berecibar, E. Nanini-Maury, J. C. W. Chan, P. Van den Bossche, ... and N. Omar, Random forest regression for online capacity estimation of lithium-ion batteries. Applied energy, 232 (2018) 197-210.
[28] B. Mallala, A. I. U. Ahmed, S. V. Pamidi, M. O. Faruque and R. Reddy, Forecasting global sustainable energy from renewable sources using random forest algorithm. Results in Engineering, 25 (2025) 103789.
[29] B. H. Massam, Multi-criteria decision making (MCDM) techniques in planning. Progress in planning, 30 (1988) 1-84.
[30] M. Matsunaga, Uncertainty management, transformational leadership, and job performance in an AI-powered organizational context. Communication monographs, 89(1) (2022) 118-139.
[31] J. M. Mendel, Computing derivatives in interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 12(1) (2004) 84-98.
[32] J. M. Mendel and X. Liu, Simplified interval type-2 fuzzy logic systems. IEEE transactions on fuzzy systems, 21(6) (2013) 1056-1069.
[33] J. M. Mendel, R. I. John and F. Liu, Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6) (2006) 808-821.
[34] A. S. Muneeza, M. Qiyas and M. A. Khan, Multi-criteria decision making based on intuitionistic cubic fuzzy numbers. Granular Computing, (2022) 1-11.
[35] M. Nie and W. W. Tan, Towards an efficient type-reduction method for interval type-2 fuzzy logic systems. In 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence) (2008) 1425-1432, IEEE.
[36] V. L. G. Nayagam, S. Muralikrishnan and G. Sivaraman, Multi-criteria decision-making method based on interval-valued intuitionistic fuzzy sets. Expert Systems with Applications, 38(3) (2011) 1464-1467.
[37] K. C. Ng and B. Abramson, Uncertainty management in expert systems. Ieee Expert, 5(2) (1990) 29-48.
[38] F. Özen, Random forest regression for prediction of Covid-19 daily cases and deaths in Turkey. Heliyon, 10(4) (2024).
[39] I. Riccioni, A. Bertolazzi, A. Tereszkiewicz, M. Szczyrbak and R. Bongelli, Uncertainty management during and about the COVID-19 pandemic. Frontiers in Communication, 9 (2024) 1357832.
[40] V. Rodriguez-Galiano, M. Sanchez-Castillo, M. Chica-Olmo and M. J. O. G. R. Chica-Rivas, Machine learning predictive models for mineral prospectivity: An evaluation of neural networks, random forest, regression trees and support vector machines. Ore Geology Reviews, 71 (2015) 804-818.
[41] A. Jog, A. Carass, S. Roy, D. L. Pham and J. L. Prince, Random forest regression for magnetic resonance image synthesis. Medical image analysis, 35 (2017) 475-488.
[42] M. Rahim, S. A. Bajri, S. Khan, H. Alqahtani and H. A. E. W. Khalifa, Innovative Multi-Criteria Group Decision Making with Interval-Valued p, q, r-Spherical Fuzzy Sets: A Case Study on Optimal Solar Energy Investment Location. International Journal of Fuzzy Systems, (2025) 1-28.
[43] C. Ruan, S. Gong and X. Chen, Multi-criteria group decision-making with extended ELECTRE III method and regret theory based on probabilistic interval-valued intuitionistic hesitant fuzzy information. Complex & Intelligent Systems, 11(1) (2025) 92.
[44] S. Salimian and S. M. Mousavi, A multi-criteria decision-making model with interval-valued intuitionistic fuzzy sets for evaluating digital technology strategies in COVID-19 pandemic under uncertainty. Arabian Journal for Science and Engineering, 48(5) (2023) 7005-7017.
[45] M. R. Segal, Machine learning benchmarks and random forest regression (2004).
[46] I. Siksnelyte-Butkiene, E. K. Zavadskas and D. Streimikiene, Multi-criteria decision-making (MCDM) for the assessment of renewable energy technologies in a household: A review. Energies, 13(5) (2020) 1164.
[47] P. F. Smith, S. Ganesh and P. Liu, A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of neuroscience methods, 220(1) (2013) 85-91.
[48] L. M. Soegianto, A. T. Hinandra, P. A. Suri and M. Fajar, Comparison of model performance on housing business using linear regression, random forest regressor, svr, and neural network. Procedia Computer Science, 245 (2024) 1139-1145.
[49] H. Taherdoost and M. Madanchian, Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia, 3(1) (2023) 77-87.
[50] G. Varathan, A review of uncertainty management approaches for active distribution system planning. Renewable and Sustainable Energy Reviews, 205(2024) 114808.
[51] J. Q. Wang, K. J. Li and H. Y. Zhang, Interval-valued intuitionistic fuzzy multi-criteria decision-making approach based on prospect score function. Knowledge-Based Systems, 27 (2012) 119-125.
[52] J. Q. Wang, Z. Q. Han and H. Y. Zhang, Multi-criteria group decision-making method based on intuitionistic interval fuzzy information. Group Decision and Negotiation, 23 (2014) 715-733.
[53] Y. J. Wang, L. J. Liu and T. C. Han, Interval-valued fuzzy multi-criteria decision-making with dependent evaluation criteria for evaluating service performance of international container ports. Journal of Marine Science and Engineering, 10(7) (2022) 991.
[54] S. Ward, C. Chapman, Stakeholders and uncertainty management in projects. Construction management and economics, 26(6) (2008) 563-577.
[55] D. Wu, J. M. Mendel, On the continuity of type-1 and interval type-2 fuzzy logic systems. IEEE Transactions on Fuzzy Systems, 19(1) (2010) 179-192.
[56] L. Xue, Y. Liu, Y. Xiong, Y. Liu, X. Cui and G. Lei, A data-driven shale gas production forecasting method based on the multi-objective random forest regression. Journal of Petroleum Science and Engineering, 196 (2021) 107801.
[57] E. K. Zavadskas and Z. Turskis, Multiple criteria decision making (MCDM) methods in economics: an overview. Technological and economic development of economy, 17(2) (2011) 397-427.
[58] Z. Zhou, C. Qiu and Y. Zhang, A comparative analysis of linear regression, neural networks and random forest regression for predicting air ozone employing soft sensor models. Scientific Reports, 13(1) (2023) 22420.
[59] L. Zhong, L. Yao, An ELECTRE I-based multi-criteria group decision making method with interval type-2 fuzzy numbers and its application to supplier selection. Applied Soft Computing, 57 (2017) 556-576.