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Open AccessArticle

Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique

1
Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia
2
Department of Economics, University of Molise, Via De Sanctis, 86100 Campobasso, Italy
3
Department of Business Administration, Azman Hashim International Business School, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia
4
Institute of Sustainable Construction Vilnius Gediminas Technical University Sauletekio al. 11, Vilnius LT-210223, Lithuania
5
CBA Research Centre, Department of Business Administration, Collage of Business and Administration, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Sustainability 2018, 10(8), 2707; https://doi.org/10.3390/su10082707
Received: 21 June 2018 / Revised: 30 July 2018 / Accepted: 30 July 2018 / Published: 1 August 2018
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. View Full-Text
Keywords: sustainability; decision making; assessment; ANFIS ensemble sustainability; decision making; assessment; ANFIS ensemble
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MDPI and ACS Style

Nilashi, M.; Cavallaro, F.; Mardani, A.; Zavadskas, E.K.; Samad, S.; Ibrahim, O. Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique. Sustainability 2018, 10, 2707. https://doi.org/10.3390/su10082707

AMA Style

Nilashi M, Cavallaro F, Mardani A, Zavadskas EK, Samad S, Ibrahim O. Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique. Sustainability. 2018; 10(8):2707. https://doi.org/10.3390/su10082707

Chicago/Turabian Style

Nilashi, Mehrbakhsh; Cavallaro, Fausto; Mardani, Abbas; Zavadskas, Edmundas K.; Samad, Sarminah; Ibrahim, Othman. 2018. "Measuring Country Sustainability Performance Using Ensembles of Neuro-Fuzzy Technique" Sustainability 10, no. 8: 2707. https://doi.org/10.3390/su10082707

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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