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ORIGINAL RESEARCH article

Front. Environ. Sci.

Sec. Toxicology, Pollution and the Environment

This article is part of the Research TopicMunicipal Solid Waste Landfills: Environmental Effects and Pollution ManagementView all 5 articles

BiAPPSS: An Enhanced Bipolar Fuzzy APPSS Framework for Sustainable Solid Waste Management

Provisionally accepted
Ajeesh Puthusserry  PauloseAjeesh Puthusserry PauloseFelix  AugustinFelix Augustin*
  • Vellore Institute of Technology - Chennai Campus, Chennai, India

The final, formatted version of the article will be published soon.

Selecting suitable sites for solid waste management (SWM) facilities is a complex decision-making problem involving conflicting socio-geographical criteria and inherent uncertainty. To address this challenge, this study proposes a novel Bipolar Association between Preference and Performance with Satisfactory Score (BiAPPSS) framework by extending the classical APPSS model into a bipolar triangular fuzzy environment. The proposed approach simultaneously captures supportive and conflicting assessments through positive and negative membership degrees, enabling a more realistic evaluation of alternative locations. Furthermore, a Bidirectional Associative Memory (BAM) network is integrated to model the interrelationships between evaluation criteria and suitable regions, enhancing the interpretability of the decision process. The applicability of the BiAPPSS–based framework is demonstrated through a real-world case study on SWM site selection in Ernakulam district, Kerala, India, where Brahmapuram is identified as the most suitable location. The robustness and validity of the proposed model are verified using comparative analysis with existing fuzzy MCDM methods, sensitivity analysis, and interpretability assessment. The results indicate that the proposed framework provides a reliable and data-driven decision-support tool for sustainable SWM planning and policy formulation.

Keywords: Bidirectional Associative Memory, Bipolar fuzzy APPSS, Bipolar fuzzy sets, Random forest regression, Site selection, solidwaste management

Received: 29 Nov 2025; Accepted: 09 Feb 2026.

Copyright: © 2026 Paulose and Augustin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Felix Augustin

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