Health Monitoring Considering Air Quality Index Prediction Using Neuro Fuzzy Inference Model: A Case Study of Lahore, Pakistan


 Air pollution, Fuzzy logic, Artificial Neural Network, Atmospheric Environment, Human Health effects, Air quality index.

How to Cite

Saima Munawar, Muhammad Hamid, Muhammad Saleem Khan, Ashfaq Ahmed, & Noreen Hameed. (2017). Health Monitoring Considering Air Quality Index Prediction Using Neuro Fuzzy Inference Model: A Case Study of Lahore, Pakistan. Journal of Basic & Applied Sciences, 13, 123–132.


For many years, improving air quality has been great attention of the whole world. It has been recognized that air pollution as a hypothetically hazardous type of environmental pollution and polluted air directly affects the human health. In Asian countries, it has converged less attention of ever growing most alarming and hazardous issue of air pollution. This paper presents a case study of Lahore city of Pakistan for the prediction of Air Quality Index (AQI) using hybrid approach of Neuro Fuzzy (NF) inference system. The ambient air data of Lahore was taken from the Environmental Protection | department (EPD) working under government of the Punjab. For results evaluation, data was recorded at different station in the period from April 2007 to May 2015. The fuzzy rules have been generated according to the Pakistan Environmental Protection Agency (PAK-EPA) standard of AQI. The NF Inference Model took the air pollutants such as Particulate Matter (PM2.5), Ozone (O3), Carbon Monoxide (CO), Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2) as inputs and predicted the air quality index as good, moderate, or unhealthy air. The results showed that NF based AQI prediction model classifies the AQI proficiently, robustly, and accurately as compared to conventional method.


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Copyright (c) 2017 Saima Munawar, Muhammad Hamid, Muhammad Saleem Khan, Ashfaq Ahmed , Noreen Hameed