Air quality modelling using long short-term memory (LSTM) over NCT-Delhi, India
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Ahn J, Shin D, Kim K, Yang J (2017) Indoor air quality analysis using deep learning with sensor data. Sensors 17:2476
Almaraz M, Bai E, Wang C, Trousdell J, Conley S, Faloona I, Houlton BZ (2018) Agriculture is a major source of NOx pollution in California. Sci Adv 4:eaao3477. https://doi.org/10.1126/sciadv.aao3477
Athanasiadis IN, Kaburlasos VG, Mitkas PA, Petridis V (2003) Applying machine learning techniques on air quality data for real-time decision support. In: First international NAISO symposium on information technologies in environmental engineering (ITEE’2003), Gdansk, Poland. Citeseer
Automotive Research Association of India (2007) Air quality monitoring project-Indian clean air programme (ICAP). Draft Rep. on emission factor development for Indian vehicles, Pune
Bao W, Yue J, Rao Y (2017) A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS One 12:e0180944
Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5:157–166
Briggs DJ, de Hoogh C, Gulliver J, Wills J, Elliott P, Kingham S, Smallbone K (2000) A regression-based method for mapping traffic-related air pollution: application and testing in four contrasting urban environments. Sci Total Environ 253:151–167. https://doi.org/10.1016/S0048-9697(00)00429-0
Chugh S, Kumar P, Muralidharan M, et al (2012) Development of Delhi driving cycle: a tool for realistic assessment of exhaust emissions from passenger cars in Delhi. SAE Technical Paper
Fan J, Li Q, Hou J et al (2017) A spatiotemporal prediction framework for air pollution based on deep RNN. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 4:15
Fu M, Wang W, Le Z, Khorram MS (2015) Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model. Neural Comput & Applic 26:1789–1797
Ghasemi A, Amanollahi J (2019) Integration of ANFIS model and forward selection method for air quality forecasting. Air Qual Atmos Health 12:59–72. https://doi.org/10.1007/s11869-018-0630-0
Gokhale S, Pandian S (2007) A semi-empirical box modeling approach for predicting the carbon monoxide concentrations at an urban traffic intersection. Atmos Environ 41:7940–7950
Graves A, Mohamed A, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: Acoustics, speech and signal processing (icassp), 2013 IEEE international conference on. IEEE, pp 6645–6649
Gurjar BR, Ravindra K, Nagpure AS (2016) Air pollution trends over Indian megacities and their local-to-global implications. Atmos Environ 142:475–495
Gurjar BR, Van Aardenne JA, Lelieveld J, Mohan M (2004) Emission estimates and trends (1990–2000) for megacity Delhi and implications. Atmos Environ 38:5663–5681
Guttikunda SK, Calori G (2013) A GIS based emissions inventory at 1 km × 1 km spatial resolution for air pollution analysis in Delhi, India. Atmos Environ 67:101–111
Guttikunda SK, Goel R, Pant P (2014) Nature of air pollution, emission sources, and management in the Indian cities. Atmos Environ 95:501–510. https://doi.org/10.1016/j.atmosenv.2014.07.006
Guttikunda SK, Gurjar BR (2012) Role of meteorology in seasonality of air pollution in megacity Delhi, India. Environ Monit Assess 184:3199–3211
PressTrust of India (2018) 40 pc of India’s population likely to reside in cities by 2030: Puri. Press Trust India, India Today
Jain S, Khare M (2010) Adaptive neuro-fuzzy modeling for prediction of ambient CO concentration at urban intersections and roadways. Air Qual Atmos Health 3:203–212. https://doi.org/10.1007/s11869-010-0073-8
Kalapanidas E, Avouris N (2001) Short-term air quality prediction using a case-based classifier. Environ Model Softw 16:263–272
Kim MH, Kim YS, Lim J, Kim JT, Sung SW, Yoo CK (2010) Data-driven prediction model of indoor air quality in an underground space. Korean J Chem Eng 27:1675–1680
Kurt A, Oktay AB (2010) Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks. Expert Syst Appl 37:7986–7992
Legates DR, McCabe GJ Jr (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241
Li X, Peng L, Yao X, Cui S, Hu Y, You C, Chi T (2017) Long short-term memory neural network for air pollutant concentration predictions: method development and evaluation. Environ Pollut 231:997–1004
Mallet V, Sportisse B (2008) Air quality modeling: from deterministic to stochastic approaches. Comput Math Appl 55:2329–2337
Mayer H, Gomez F, Wierstra D, Nagy I, Knoll A, Schmidhuber J (2008) A system for robotic heart surgery that learns to tie knots using recurrent neural networks. Adv Robot 22:1521–1537
Mikolov T, Joulin A, Chopra S, Mathieu M, Ranzato MA (2014) Learning longer memory in recurrent neural networks. arXiv preprint arXiv:1412.7753
Milionis AE, Davies TD (1994) Regression and stochastic models for air pollution—I. Review, comments and suggestions. Atmos Environ 28:2801–2810
Ni XY, Huang H, Du WP (2017) Relevance analysis and short-term prediction of PM2. 5 concentrations in Beijing based on multi-source data. Atmos Environ 150:146–161
Pardo E, Malpica N (2017) Air quality forecasting in Madrid using long short-term memory networks. In: International Work-Conference on the Interplay Between Natural and Artificial Computation. Springer, pp 232–239
PTI (2018). 40 pc of India’s population likely to reside in cities by 2030: Puri. Press Trust India, India Today.
Schnelle KB, Dey PR (2000) Atmospheric dispersion modeling compliance guide. McGraw-Hill, New York
Sekar C, Gurjar BR, Ojha CSP, Goyal MK (2016a) Potential assessment of neural network and decision tree algorithms for forecasting ambient PM2.5 and CO concentrations: case study. J Hazard Toxic Radioact Waste 20:A5015001. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000276
Sekar C, Ojha CSP, Gurjar BR, Goyal MK (2016b) Modeling and prediction of hourly ambient ozone (O3) and oxides of nitrogen (NOx) concentrations using artificial neural network and decision tree algorithms for an urban intersection in India. J Hazard Toxic Radioact Waste 20:A4015001. https://doi.org/10.1061/(ASCE)HZ.2153-5515.0000270
Sønderby SK, Sønderby CK, Nielsen H, Winther O (2015) Convolutional LSTM networks for subcellular localization of proteins. In: International Conference on Algorithms for Computational Biology. Springer, pp 68–80
Srivastava A, Jain VK (2005) A study to characterize the influence of outdoor SPM and associated metals on indoor environment in Delhi. J Environ Sci Eng 47:222–231
UN (2018) 2018 revision of world urbanization prospects. https://www.un.org/development/desa/publications/2018-revision-of-world-urbanization-prospects.html . Accessed 14 April 2019
West JJ, Naik V, Horowitz LW, Fiore AM (2009) Effect of regional precursor emission controls on long-range ozone transport—part 1: short-term changes in ozone air quality. Atmos Chem Phys 9:6077–6093
WHO, 2018. Global Ambient Air Quality Database (update 2018). World Health Orgination.
Zhang J, Zhu Y, Zhang X, Ye M, Yang J (2018) Developing a long short-term memory (LSTM) based model for predicting water table depth in agricultural areas. J Hydrol 561:918–929