Abstract
This study aims to develop a new approach based on machine learning techniques to assess sustainability performance. Two main dimensions of sustainability, ecological sustainability, and human sustainability, were considered in this study. A set of sustainability indicators was used, and the research method in this study was developed using cluster analysis and prediction learning techniques. A Self-Organizing Map (SOM) was applied for data clustering, while Classification and Regression Trees (CART) were applied to assess sustainability performance. The proposed method was evaluated through Sustainability Assessment by Fuzzy Evaluation (SAFE) dataset, which comprises various indicators of sustainability performance in 128 countries. Eight clusters from the data were found through the SOM clustering technique. A prediction model was found in each cluster through the CART technique. In addition, an ensemble of CART was constructed in each cluster of SOM to increase the prediction accuracy of CART. All prediction models were assessed through the adjusted coefficient of determination approach. The results demonstrated that the prediction accuracy values were high in all CART models. The results indicated that the method developed by ensembles of CART and clustering provide higher prediction accuracy than individual CART models. The main advantage of integrating the proposed method is its ability to automate decision rules from big data for prediction models. The method proposed in this study could be implemented as an effective tool for sustainability performance assessment.
Highlights
The Classification and Regression Trees (CART) models were developed for ensemble learning
The result of this study demonstrated that the hybrid approach that combines clustering and prediction machine learning techniques can improve the prediction accuracy of Adaptive Neuro-Fuzzy Inference System (ANFIS)
The results of this study’s analysis revealed that the combination of Self-Organizing Map (SOM) and CART techniques with the aid of ensemble learning resulted in a more superior performance compared to CART, ANFIS, Neural Network (NN), Multiple Linear Regression (MLR), Fuzzy C-Means + CART, and Fuzzy C-Means + ANFIS in the measurement of country sustainability performance
Summary
The term sustainability, which means “to hold up or support”, was emerged in the 18th century for forest management issues [1]. Sustainability has concentrated on various fields, such as the environment, agriculture, and social sciences. Sustainability assessment has played an important role in the improvement of the decision making process. This process mainly involves intragenerational and intergenerational considerations, which lead to (1) enhanced monitoring and communication of results, (2) a 4.0/).
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
