Fuzzy Inference System Tree with Particle Swarm Optimization and Genetic Algorithm: A novel approach for PM10 forecasting

Abstract World health organization's estimates reveal that air pollution kills almost 6.5 million people in the world every year. As human beings, on average, spend 80–90% of their routine time in closed spaces, indoor air pollution has been a prime concern for their health and well-being. Primary and secondary pollutants are present in indoor environments, and they leave a considerable impact on human health. However, PM10 has attracted special scientific and legislative attention due to its close association with chronic health problems such as respiratory illness, lung cancer, and asthma attacks. Therefore, it is critical to develop a reliable model to analyze PM10 pollutants in the indoor environment so that building occupants can take relevant preventive measures. This paper focuses on the monitoring and predicting PM10 pollutant concentration in the indoor environments using the Fuzzy Inference System Tree (FIST) model. The forecasting model was trained using five different input parameters (PM2.5, CO2, VOC, temperature, and humidity) while considering PM10 as the target variable. The system performance was measured in terms of four performance indicators where MSE = 1.8126; MAE = 1.1821; MAPE = 4.4372%; RMSE = 1.3463 using normalized data. The model performance was further improved using Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Results show that the proposed aggregated FIS optimized with GA (RMSE = 1.2101) outperformed the PSO (RMSE = 1.2202) based model in terms of performance indicators. The proposed model can be installed in real-time environments to forecast PM10 concentration for improved public health and well-being.

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