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Prediction of Eco Sustainability Component Using Fuzzy Z Numbers Based Ratio Analysis and Interval Type 3 Fuzzy Logic System

Publication Type : Journal Article

Publisher : Elsevier BV

Source : Journal of Cleaner Production

Url : https://doi.org/10.1016/j.jclepro.2024.144125

Keywords : Z-Fuzzy SWARA, Interval type 3 fuzzy logic, Eco-sustainability, Multi criteria group decision making, AQI, WQI

Campus : Faridabad

Year : 2024

Abstract : Amid rapid global urbanization, understanding eco-sustainability challenges in megacities like Delhi is crucial both locally and globally. This study introduces a novel multi-criteria group decision-making approach, the Z-Fuzzy Step-Wise Weight Assessment Ratio Analysis (SWARA), to address these complex challenges. This method prioritizes key eco-sustainability components like air quality index (AQI), water quality index (WQI), Temperature, and Precipitation, based on their impact, aiming to enhance resource allocation and environmental quality. Criteria weights are determined using fuzzy analytical hierarchical processing with Z-numbers, which manage decision-makers' uncertainty through a reliability function, illustrating their confidence levels and supporting the adaptability and effectiveness of eco-sustainability measures. A case study is conducted on seven sub-cities in Delhi examining their unique environmental impacts and urban sustainability challenges. Moreover, the most effective component of eco-sustainability say AQI is also dependent on some factors like PM10, PM2.5, SO2, O3, NO2, and CO. Considering these factors as inputs, an interval type-3 fuzzy logic system (IT3FLS) has been constructed to predict AQI, supporting timely interventions and improvements in eco-sustainability management. The IT3FLS model demonstrated exceptional precision across all Delhi sub-cities, with low root means square error (<0.05), and high R2 values (>0.94), affirming its effectiveness. To validate the Z-Fuzzy SWARA rankings, we compared them with existing Z-Fuzzy methods, consistently ranking AQI first. The IT3FLS's AQI predictions were also compared with those from fuzzy logic and advanced machine learning algorithms, demonstrating superior performance. Finally, the study underscores the managerial implications, emphasizing their role in guiding decision-making and strategy implementation.

Cite this Research Publication : Anirban Tarafdar, Kanika, Azharuddin Shaikh, Pinki Majumder, Dragan Pamucar, Vladimir Simic, Uttam Kumar Bera, Prediction of eco sustainability component using fuzzy Z numbers based ratio analysis and interval type 3 fuzzy logic system, Journal of Cleaner Production, Elsevier BV, 2024, https://doi.org/10.1016/j.jclepro.2024.144125

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