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Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

Author

Listed:
  • Mehrbakhsh Nilashi

    (Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia)

  • Fausto Cavallaro

    (Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy)

  • Abbas Mardani

    (Department of Business Administration, Azman Hashim International Business School, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia)

  • Edmundas Kazimieras Zavadskas

    (Institute of Sustainable Construction Vilnius Gediminas Technical University Sauletekio al. 11, Vilnius LT-210223, Lithuania)

  • Sarminah Samad

    (CBA Research Centre, Department of Business Administration, Collage of Business and Administration, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

  • Othman Ibrahim

    (Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia)

Abstract

Global warming is one of the most important challenges nowadays. Sustainability practices and technologies have been proven to significantly reduce the amount of energy consumed and incur economic savings. Sustainability assessment tools and methods have been developed to support decision makers in evaluating the developments in sustainable technology. Several sustainability assessment tools and methods have been developed by fuzzy logic and neural network machine learning techniques. However, a combination of neural network and fuzzy logic, neuro-fuzzy, and the ensemble learning of this technique has been rarely explored when developing sustainability assessment methods. In addition, most of the methods developed in the literature solely rely on fuzzy logic. The main shortcoming of solely using the fuzzy logic rule-based technique is that it cannot automatically learn from the data. This problem of fuzzy logic has been solved by the use of neural networks in many real-world problems. The combination of these two techniques will take the advantages of both to precisely predict the output of a system. In addition, combining the outputs of several predictors can result in an improved accuracy in complex systems. This study accordingly aims to propose an accurate method for measuring countries’ sustainability performance using a set of real-world data of the sustainability indicators. The adaptive neuro-fuzzy inference system (ANFIS) technique was used for discovering the fuzzy rules from data from 128 countries, and ensemble learning was used for measuring the countries’ sustainability performance. The proposed method aims to provide the country rankings in term of sustainability. The results of this research show that the method has potential to be effectively implemented as a decision-making tool for measuring countries’ sustainability performance.

Suggested Citation

  • Mehrbakhsh Nilashi & Fausto Cavallaro & Abbas Mardani & Edmundas Kazimieras Zavadskas & Sarminah Samad & Othman Ibrahim, 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique," Sustainability, MDPI, vol. 10(8), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:8:p:2707-:d:161375
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    3. Ramin Gharizadeh Beiragh & Reza Alizadeh & Saeid Shafiei Kaleibari & Fausto Cavallaro & Sarfaraz Hashemkhani Zolfani & Romualdas Bausys & Abbas Mardani, 2020. "An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies," Sustainability, MDPI, vol. 12(3), pages 1-24, January.
    4. Jasna Petković & Nataša Petrović & Ivana Dragović & Kristina Stanojević & Jelena Andreja Radaković & Tatjana Borojević & Mirjana Kljajić Borštnar, 2019. "Youth and forecasting of sustainable development pillars: An adaptive neuro-fuzzy inference system approach," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-25, June.
    5. Wojciech Sałabun & Krzysztof Palczewski & Jarosław Wątróbski, 2019. "Multicriteria Approach to Sustainable Transport Evaluation under Incomplete Knowledge: Electric Bikes Case Study," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    6. Ahmed Elbeltagi & R. K. Jaiswal & R. V. Galkate & Manish Kumar & A. K. Lohani & Jaiveer Tyagi, 2023. "Modeling Soil Water Retention Under Different Pressures Using Adaptive Neuro-Fuzzy Inference System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(4), pages 1519-1538, March.
    7. Michael Gr. Voskoglou, 2019. "Methods for Assessing Human–Machine Performance under Fuzzy Conditions," Mathematics, MDPI, vol. 7(3), pages 1-21, March.

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