Optimal Prediction of Air Quality Index in Metropolitan Cities Using Fuzzy Time Series with Deep Learning Approach
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Department of Computer Science, Sree Saraswathi Thyagaraja College, Bharathiar University, Pollachi, Tamil Nadu, India
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Asha Unnikrishnan   

Department of Computer Science, Sree Saraswathi Thyagaraja College, Bharathiar University, Pollachi, Tamil Nadu, India
Ecol. Eng. Environ. Technol. 2024; 1:341-359
The advancement of science and technology has led to the custom of industry, transportation, and other sectors to release a great amount of pollutants into the atmosphere, causing air pollution. Predicting the Air Quality Index (AQI) with high accuracy is just as crucial as predicting the weather. The research selects a few potential meteorological parameters and historical data after taking into account a variety of complex factors to accurately anticipate AQI. The dataset was gathered, pre-processed to substitute Missing Values (MV) and eliminate redundant information, and before being applied to prediction the AQI. The data was collected from 2019 to 2022 to analyse AQI founded on Time Series Forecasting (TSF). Many AQI parameters, including accumulated precipitation, the daily normal temperature, and prevailing winds, are lacking in this study. Average daily temperature, and direction of wind. To preserve the characteristics of the time series, implement kNN accusation to fill in the MV and integrate Principal Component Analysis (PCA) to decrease the noise of data to recover the accuracy of AQI prediction. However, the majority of research is limited due to a lack of panel data, which means that characteristics such as seasonal behaviour cannot be taken into account. Consequently, the research introduces a TSF grounded on Seasonal Autoregressive Integrated Moving Average (SARIMA) and Stochastic Fuzzy Time Series (SFTS). The Stacked Dilated Convolution Technique (SDCT) effectively extracts the time autocorrelation while the time attention module concentrates on the time intervals that were significantly linked with each instant. To control strongly connected features in the data set, the Spearman Rank Correlation Coefficient (SRCC) was utilised. The selected features included SO2, CO and O3, NO2, PM10 and PM2.5, temperature, pressure, stickiness, airstream speed and weather, and rainfall. Additionally, to estimate the AQI and SO2, PM10, PM2.5, NO2, CO, and O3concentration from 2019 to 2022, the data of climatological elements after PCA and historical AQI are input into the Multiple Linear Regression (MLR) techniques with a Temporal Convolution Network (TCN) built Deep Learning Model (DLM). The proposed DLM springs a correct and detailed assessment for AQI prediction.
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