ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

Abstract

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM₁₀, PM_2.5, CO₂, CO, tVOC, and NO₂). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM₁₀, PM_2.5, CO₂, CO, tVOC, and NO₂, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

Keywords: fuzzy inference system; indoor air quality; optimization; pollution; public health.

Figure 1

Monitoring site location displayed using Google Maps.

Figure 2

Hardware monitoring system architecture.

Figure 3

Pearson correlation between parameters.

Figure 4

Membership functions for air quality parameters: (a) Temp, (b) PM_2.5, (c) CO₂, (d) tVOC, (e) CO, (f) NO₂, (g) Hum and (h) PM₁₀.

Figure 5

General flow of FIS system with PSO rule learning.

Figure 6

General architecture of DFIST model.

Figure 7

Full system architecture for proposed ADFIST IAQ parameter prediction approach.