A deep learning approach for prediction of air quality index in a metropolitan city

Abstract In India, the Central and State Pollution Control Boards have commissioned the National Air Monitoring Program (NAMP) which covers 240 cities with 342 monitoring stations. Air Quality Index (AQI) has been categorized into different groups. To predict the AQI in Chennai city, the Dataset was collected, then preprocessed to replace missing values and remove redundant data. The mean, mean square error and standard deviation are extracted using the Grey Level Co-occurrence Matrix (GLCM). The combination of Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) based deep learning model is used to classify the AQI values. The proposed deep learning model gives an accurate and specific value for AQI on the city’s specified location compared to the existing techniques. The prediction accuracy is improved in the proposed deep learning method, which will caution the public to reduce to an acceptable level. The deep learning mechanism predicts the AQI values accurately and helps to plan the metropolitan city for sustainable development. The expected AQI value can control the pollution level by incorporating road traffic signal coordination, encouraging the people to use public transportation, and planting more trees on some locations.

[1]  Jamil Amanollahi,et al.  Integration of ANFIS model and forward selection method for air quality forecasting , 2018, Air Quality, Atmosphere & Health.

[2]  Tingli Su,et al.  Deep Hybrid Model Based on EMD with Classification by Frequency Characteristics for Long-Term Air Quality Prediction , 2020, Mathematics.

[3]  Samaher Al-Janabi,et al.  A new method for prediction of air pollution based on intelligent computation , 2020, Soft Comput..

[4]  R. Kingsy Grace,et al.  A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems , 2019, Wirel. Pers. Commun..

[5]  C. Jang,et al.  A system for developing and projecting PM2.5 spatial fields to correspond to just meeting national ambient air quality standards , 2019, Atmospheric Environment: X.

[6]  Ashkan Sami,et al.  Towards Sustainable Smart City by Particulate Matter Prediction Using Urban Big Data, Excluding Expensive Air Pollution Infrastructures , 2019, Big Data Res..

[7]  Samaher Al-Janabi,et al.  A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation , 2019, Soft Computing.

[8]  F. Ferreira,et al.  Macao air quality forecast using statistical methods , 2019, Air Quality, Atmosphere & Health.

[9]  Zhiwei Lian,et al.  Effects of exposure to carbon dioxide and bioeffluents on perceived air quality, self‐assessed acute health symptoms, and cognitive performance , 2017, Indoor air.

[10]  Bruce Misstear,et al.  The potential impacts of different traffic management strategies on air pollution and public health for a more sustainable city: A modelling case study from Dublin, Ireland , 2020 .

[11]  Sanjay Agrawal,et al.  Introduction to Condition Monitoring of PV System , 2020 .

[12]  Claudio Del Pero,et al.  Smart buildings features and key performance indicators: A review , 2020 .

[13]  Jong Hyuk Park,et al.  A deep learning-based IoT-oriented infrastructure for secure smart City , 2020 .

[14]  Hossein Nematzadeh,et al.  Predicting air pollution in Tehran: Genetic algorithm and back propagation neural network , 2016 .

[15]  Hufang Yang,et al.  A dynamic evaluation framework for ambient air pollution monitoring , 2019, Applied Mathematical Modelling.

[16]  H. Tenhunen,et al.  Edge AI in Smart Farming IoT: CNNs at the Edge and Fog Computing with LoRa , 2019, 2019 IEEE AFRICON.

[17]  Jianqiang Li,et al.  A Sequence-to-Sequence Air Quality Predictor Based on the n-Step Recurrent Prediction , 2019, IEEE Access.

[18]  Samaher AlJanabi,et al.  Pragmatic Method Based on Intelligent Big Data Analytics to Prediction Air Pollution , 2019, Big Data and Networks Technologies.

[19]  Congcong Wen,et al.  A novel spatiotemporal convolutional long short-term neural network for air pollution prediction. , 2019, The Science of the total environment.

[20]  S. Nath,et al.  Artificial Neural Networks Based Condition Monitoring of Air Pollutants for Allahabad Cities in India , 2020 .

[21]  Armin Sorooshian,et al.  Air pollution prediction by using an artificial neural network model , 2019, Clean Technologies and Environmental Policy.

[22]  R. Srivastava,et al.  Assessment of the ambient air quality at the Integrated Industrial Estate‐Pantnagar through the air quality index (AQI) and exceedence factor (EF) , 2011 .

[23]  John Kaiser Calautit,et al.  A review of artificial neural network models for ambient air pollution prediction , 2019, Environ. Model. Softw..

[24]  Zhihua Xu,et al.  Extending the theory of planned behavior to predict public participation behavior in air pollution control: Beijing, China , 2019, Journal of Environmental Planning and Management.

[25]  Robert Bembenik,et al.  Air pollution prediction with clustering-based ensemble of evolving spiking neural networks and a case study for London area , 2019, Environ. Model. Softw..

[26]  Samaher Al-Janabi,et al.  Evaluation prediction techniques to achievement an optimal biomedical analysis , 2019, Int. J. Grid Util. Comput..

[27]  Yuexiong Ding,et al.  A Lag-FLSTM deep learning network based on Bayesian Optimization for multi-sequential-variant PM2.5 prediction , 2020 .

[28]  Jing Liu,et al.  Dynamic prediction of PM2.5 diffusion in urban residential areas in severely cold regions based on an improved urban canopy model , 2020 .