基于本体的知识建模与考虑不确定性的户外空气质量评估方法：使用加权区间型2模糊逻辑
Ontology-Based Knowledge Modeling and Uncertainty-Aware Outdoor Air Quality Assessment Using Weighted Interval Type-2 Fuzzy Logic
AI总结：
本文提出一种结合本体知识建模和加权区间型2模糊逻辑的混合框架，用于提高户外空气质量评估的可靠性与不确定性处理能力，同时支持可解释的语义推理和智能决策支持。
摘要：
室外空气污染是环境和公共健康的主要关注点，特别是在快速城市化地区。印度空气质量指数（IND-AQI）由中央污染控制委员会（CPCB）开发，是基于PM2.5、PM10、二氧化氮（NO2）、二氧化硫（SO2）、臭氧（O3）、一氧化碳（CO）和氨（NH3）等污染物的标准报告系统。然而，传统的AQI计算使用清晰阈值和确定性聚合规则，这些方法不适用于处理不确定性和类别之间的过渡。为了克服这些限制，本文提出了一种混合本体基于的不确定性感知框架，结合加权区间型2模糊逻辑与语义知识建模。区间型2模糊集用于建模接近AQI类别边界处的不确定性，而污染物重要性权重通过区间型2模糊分析层次过程（IT2-FAHP）确定，以反映其相对健康影响。此外，开发了一个基于OWL的空气质量本体，扩展了语义传感器网络（SSN）本体，以表示污染物、监测站、AQI类别、监管标准和环境治理行动。使用SWRL规则进行语义推理，并通过SPARQL查询验证以推断AQI类别、健康风险和推荐的缓解措施。使用CPCB空气质量数据集的实验评估表明，所提出的框架相比传统清晰和类型1模糊方法，提高了AQI分类的可靠性和不确定性处理能力，同时实现了可解释的语义推理和智能决策支持。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
