用模糊逻辑与知识图谱提升空气质量评估的准确性与可解释性
Ontology-Based Knowledge Modeling and Uncertainty-Aware Outdoor Air Quality Assessment Using Weighted Interval Type-2 Fuzzy Logic
- 结合本体建模与加权区间二型模糊逻辑，处理污染物数据的不确定性
- 在印度空气指数数据上验证，分类可靠性显著优于传统方法
- 适合环境监测、智能决策系统研发人员参考
室外空气污染是快速城市化地区环境与公共健康的主要威胁。印度中央污染控制委员会（CPCB）开发的印度空气质量指数（IND-AQI）基于PM2.5、PM10、NO2、SO2、O3、CO和NH3等污染物，但传统计算采用确定性阈值与聚合规则，难以应对类别边界附近的不确定性。为此，本文提出一种融合本体建模与加权区间二型模糊逻辑的混合框架。利用区间二型模糊集建模接近AQI类别的不确定性，通过区间二型模糊层次分析法（IT2-FAHP）确定各污染物权重以反映其健康影响。同时构建基于OWL的空气质量本体，扩展语义传感器网络（SSN）本体，表示污染物、监测站、AQI类别、监管标准及环境治理行动。使用SWRL规则进行语义推理，并通过SPARQL查询验证，可推断AQI等级、健康风险与建议缓解措施。在CPCB空气质量数据集上的实验表明，该框架在分类可靠性与不确定性处理方面优于传统精确与一型模糊方法，同时支持可解释推理与智能决策支持。
原文摘要 · Abstract (English)
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
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