References
- Pouyanfar, Samira, et al. “A survey on deep learning: Algorithms, techniques, and applications.” ACM Computing Surveys (2018): 1-36.
- Shrestha, Ajay, and Ausif Mahmood. “Review of deep learning algorithms and architectures.” IEEE access 7 (2019): 53040-53065. doi: 10.1109/ACCESS.2019.2912200
- Manna, Souvik, Suman Sankar Bhunia, and Nandini Mukherjee. “Vehicular pollution monitoring using IoT.” International Conference on Recent Advances and Innovations in Engineering (ICRAIE-2014). IEEE, 2014.
- Marinov, Marin B., et al. “Air quality monitoring in urban environments.” 2016 39th International Spring Seminar on Electronics Technology (ISSE). IEEE, 2016.
- Habibi, Roya, et al. “An assessment of spatial pattern characterization of air pollution: A case study of CO and PM2. 5 in Tehran, Iran.” ISPRS international journal of Geo-information 6.9 (2017): 270. doi: 10.3390/ijgi6090270
- Zhou, Yuchao, et al. “Data-driven air quality characterization for urban environments: A case study.” IEEE Access 6 (2018): 77996-78006. doi: 10.1109/ACCESS.2018.2884647
- Karmiani, Divit, et al. “Comparison of predictive algorithms: backpropagation, SVM, LSTM and Kalman Filter for stock market.” 2019 Amity International Conference on Artificial Intelligence. IEEE, 2019.
- Tao, Qing, et al. “Air pollution forecasting using a deep learning model based on 1D convnets and bidirectional GRU.” IEEE access 7 (2019).
- Gu, Ke, Junfei Qiao, and Weisi Lin. “Recurrent air quality predictor based on meteorology-and pollution-related factors.” IEEE Transactions on Industrial Informatics 14.9 (2018): 3946-3955. doi: 10.1109/TII.2018.2793950
- Zhao, Zheng, et al. “LSTM network: a deep learning approach for short-term traffic forecast.” IET Intelligent Transport Systems 11.2 (2017). doi: 10.1049/iet-its.2016.0208
- Moses, Leeban. “IoT enabled Environmental Air Pollution Monitoring and Rerouting system using Machine learning algorithms.” IOP Conference Series: Materials Science and Engineering. Vol. 955. No. 1, 2020. doi: 10.1088/1757-899X/955/1/012005
- Mao, Wenjing, et al. “A hybrid integrated deep learning model for predicting various air pollutants.” GIScience & Remote Sensing 58.8 (2021): 1395-1412. doi: 10.1080/15481603.2021.1988429
- Centre Control Room for Air Quality Management. (2020) Available at: <https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqmlanding/data>
- Copernicus Climate Change Service (C3 S) (2017): ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. (2017).
- Moderate Resolution Imaging Resolution Spectroradiometer. Available at: <https://modis.gsfc.nasa.gov/> [Accessed 28 May 2020].
- Open Govt Data Platform India. Available at: <http://data.gov.in/> 2020
- Air Quality and How to measure it: The Air Quality Index. Available at: <https://environmental-conscience.com/air-quality-and-air-quality-index/ >