Ontology-Based Knowledge Modeling and Uncertainty-Aware Outdoor Air Quality Assessment Using Weighted Interval Type-2 Fuzzy Logic

2026-03-20

AI Summary

This study addresses the limitations of the traditional Indian Air Quality Index (IND-AQI), which relies on rigid thresholds and struggles to handle uncertainty when pollutant concentrations lie near category boundaries. To overcome this, the authors propose a novel outdoor air quality assessment framework that integrates weighted interval type-2 fuzzy logic with ontology-based knowledge modeling. Specifically, they introduce the first integration of interval type-2 fuzzy sets with OWL ontologies, incorporate an IT2-FAHP mechanism for dynamic weight assignment, and extend the SSN ontology to construct a domain-specific knowledge model enabling SWRL-based semantic reasoning and SPARQL querying. Experimental results on the CPCB dataset demonstrate that the proposed approach significantly outperforms both the conventional IND-AQI and type-1 fuzzy systems, offering superior accuracy in AQI classification, enhanced uncertainty modeling, and more interpretable intelligent decision-making.

Technology Category

• Knowledge Representation and Reasoning: Qualitative Reasoning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Outdoor air pollution is a major concern for the environment and public health, especially in areas where urbanization is taking place rapidly. The Indian Air Quality Index (IND-AQI), developed by the Central Pollution Control Board (CPCB), is a standardized reporting system for air quality based on pollutants such as PM2.5, PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), carbon monoxide (CO), and ammonia (NH3). However, the traditional calculation of the AQI uses crisp thresholds and deterministic aggregation rules, which are not suitable for handling uncertainty and transitions between classes. To address these limitations, this study proposes a hybrid ontology-based uncertainty-aware framework integrating Weighted Interval Type-2 Fuzzy Logic with semantic knowledge modeling. Interval Type-2 fuzzy sets are used to model uncertainty near AQI class boundaries, while pollutant importance weights are determined using Interval Type-2 Fuzzy Analytic Hierarchy Process (IT2-FAHP) to reflect their relative health impacts. In addition, an OWL-based air quality ontology extending the Semantic Sensor Network (SSN) ontology is developed to represent pollutants, monitoring stations, AQI categories, regulatory standards, and environmental governance actions. Semantic reasoning is implemented using SWRL rules and validated through SPARQL queries to infer AQI categories, health risks, and recommended mitigation actions. Experimental evaluation using CPCB air quality datasets demonstrates that the proposed framework improves AQI classification reliability and uncertainty handling compared with traditional crisp and Type-1 fuzzy approaches, while enabling explainable semantic reasoning and intelligent decision support for air quality monitoring systems

Problem

• Air Quality Index
• Uncertainty Handling
• Fuzzy Logic
• Ontology
• Environmental Monitoring

Innovation

• Interval Type-2 Fuzzy Logic
• Ontology-Based Knowledge Modeling
• Uncertainty-Aware AQI Assessment
• IT2-FAHP
• Semantic Reasoning