International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 11, No. 2, 8 Feb. 2019
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Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks
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Author(s)
K Srinivasa Rao
1,*
G. Lavanya Devi
1
N. Ramesh
1
Index Terms
Air quality, air pollution, prediction, environment, deep learning, recurrent neural networks, long short term memory
Abstract
The research activity considered in this paper concerns about efficient approach for modeling and prediction of air quality. Poor air quality is an environmental hazard that has become a great challenge across the globe. Therefore, ambient air quality assessment and prediction has become a significant area of study. In general, air quality refers to quantification of pollution free air in a particular location. It is determined by measuring different types of pollution indicators in the atmosphere. Traditional approaches depend on numerical methods to estimate the air pollutant concentration and require lots of computing power. Moreover, these methods cannot draw insights from the abundant data available. To address this issue, the proposed study puts forward a deep learning approach for quantification and prediction of ambient air quality. Recurrent neural networks (RNN) based framework with special structured memory cells known as Long Short Term Memory (LSTM) is proposed to capture the dependencies in various pollutants and to perform air quality prediction. Real time dataset of the city Visakhapatnam having a record of 12 pollutants was considered for the study. Modeling of temporal sequence data of each pollutant was performed for forecasting hourly based concentrations. Experimental results show that proposed RNN-LSTM frame work attained higher accuracy in estimating hourly based air ambience. Further, this model may be enhanced by adopting bidirectional mechanism in recurrent layer.
Cite This Paper
K Srinivasa Rao, G. Lavanya Devi, N. Ramesh, "Air Quality Prediction in Visakhapatnam with LSTM based Recurrent Neural Networks", International Journal of Intelligent Systems and Applications(IJISA), Vol.11, No.2, pp.18-24, 2019. DOI:10.5815/ijisa.2019.02.03
Reference
[1]Ganganjot Kaur Kang, Jerry Zeyu, Sancho, Shengqiang Lu, and Gang Xie. Air Quality Prediction: Big Data and Machine Learning Approaches [J]. International Journal of Environmental Science and Development, 2018, 9(1): 8-16.
[2]Niharika, Venkatadri M, and Padma S Rao. A Survey on Air Quality forecasting Techniques [J] International Journal of Computer Science and Information Technologies, 2014, .5():103-107.
[3]OriolVinyals, Alexander Toshev, SamyBengio and DumitruErhan. Show and tell: ANeural Image Caption Generator [C] in Proceedings of the IEEE Conference on Computer Vision and Pattern recognition, 2015, 3156–3164.
[4]Alex Graves, Abdel-rahman Mohamed and Geoffrey Hinton. Speech Recognition with Deep Recurrent Neural Networks[C], in Acoustics, Speech and Signal processing, IEEE, 2013, 6645– 6649.
[5]R.Collobert, JWeston, AUnified architecture for natural language processing:Deep neural networks with multitask learning[C], In Proceedings of the 25th International Conference on Machine Learning, 2008, 5-9.
[6]HongleiRen, You Song, Jingwen Wang, Yucheng Hu andJinzhi Lei. A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction[R], https://arxiv.org/pdf/1710.09543.15, April, 2018.
[7]B. Hochreiter and J. Schmidhuber. Long Short Term Memory[J], Neural Computation, 1997, 9(): 1735-1780.
[8]J. Schmidhuber. Deep Learning in Neural Networks An Overview, 2014.
[9]M. Langkvist, L. Karlsson, and A. Loutfi. A Review of Unsupervised Feature Learning and Deep Learning for Time-Series Modeling[R], Pattern Recognition Letters, June, 2014, 42: 11-24.
[10]Y. LeCun, Y. Bengio, and G. Hinton. Deep Learning[R], Nature, 2015, 521: 436-444.
[11]I. M. Coelho, V. N. Coelho, E. J. d. S. Luz, L. S. Ochi, F. G. Guimarães, and E. Rios. A GPU Deep Learning Meta Heuristic based Model for Time Series Forecasting[R], Applied Energy, 2017.
[12]Z. C. Lipton et al. A Critical Review of Recurrent Neural Networks for Sequence Learning[R], 2015.
[13]Zhongang Qi et al. Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-grained Air Quality[R], In: CoRR abs/1711.00939,arXiv: 1711.00939. URL: http://arxiv.org/abs/1711.00939,2, Nov, 2017.
[14]Vikram Reddy et al. Deep Air: Forecasting Air Pollution in Beijing, China[R], 2017.
[15]TVBPS Rama Krishna, MK Reddy, RC Reddy, RN Singh and S Devotta. Modelling of Ambient Air Quality over Visakhapatnam Bowl Area[C], Proc Indian NatnSciAcad, 2006, 72(1): 55-61.
[16]SrinivasaRao S, N. SrinivasaRajamani, and E. U. B. Reddi. Dispersal Conditions and Assimilative Capacity of Air Environment at Gajuwaka Industrial Hub in Visakhapatnam [J], International Journal of Scientific Research in Science, Engineering and Technology, 2015, 1(4).
[17]https://www.visakhapatnamsmartcity.com/
[18]https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing
[19]https://timesofindia.indiatimes.com/city/visakhapatnam/Ranked-5th-clean-city-Vizag-finds-air-pollution-on-the-rise/articleshow/51737679.cms
[20]Ian Goodfellow,YoshuaBengio and Aaron Courville. Deep Learning, MIT Press, 2016.
[21]KostandinaVeljanovska and Angel Dimoski. Air Quality Index Prediction Using Simple Machine Learning Algorithms [J], International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), January – February 2018, 7(1).
[22]Bingyue Pan. Application of XGBoost algorithm in hourly PM2.5 concentration prediction[C], IOP Conf. Series: Earth and Environmental Science 113, 2018.
[23]Jiawei Han, Micheline Kamber, and Jian Pei. Data Mining: Concepts and Techniques, 3rd Edition, The Morgan Kaufmann Series in Data Management Systems, 2011.
[24]M. Sujatha, G. Lavanya Devi, K. SrinivasaRao and N. Ramesh. Rough Set Theory Based Missing Value Imputation[J], Cognitive Science and Health Bioinformatics. Springer Briefs in Applied Sciences and Technology. Springer, Singapore, Online ISBN 978-981-10-6653-5, 2018.
[25]Rami Al-Rfou et al. Theano: A Python framework for fast computation of mathematical expressions[R],arXiv preprint arXiv:1605.02688,2016.
[26]Li.C, Hsu N.C, Tsay.S. A Study on the potential applications of satellite data in air quality monitoring and forecasting [J], Atmos.Environ, 2011, 45:3663-3675.
[27]Xiang Li et al. Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation [J], Elsevier Ltd., Environmental Pollution, September, 2017, 231: 997-1004.
[28]Ibrahim Kok et al. A Deep Learning Model for Air Quality Prediction in Smart Cities[C], IEEE International Conference on Big Data (BIGDATA), 2017.
[29]S. Sharma , U. Kalra , S. Srivathsan , K.P.S. Rana , and V. Kumar. Efficient Air Pollutants Prediction using ANFIS Trained by Modified PSO Algorithm[C], IEEE, 2015.
[30]Athanasiadasi.I.N, Kaburaos,V.G, Mittkas P.A, and Petridis.V. Applying machine learning techniques on air quality data for real-time decision support[C], ITEE, 2003.
[31]Dixian Zhu,Changjie Cai, Tianbao Yang, and Xun Zhou. A Machine learning approach for air quality prediction: Model regularization and optimization[C], Big data and cognitive computing, 2018.
[32]E.Kalapanidas and N.Avouris. Applying machine learning techniquesin air quality prediction, 1999.
[33]Box G.E.P, Jenkins G.M. Time series analysis: forecasting and control”, J.Operational Res, 1976, 22:199-201.
[34]Nieto P.G, Combarro E.F,Del Coz Diaz J.J. A SVM- based regression model to study the air quality at local scale in Oviedo urban area(Northern Spain): a case study[J], Appl.Math.Comput, 2013, 8923-8937.
[35]Athira V, Geetha P, Vinayakumar R, Soman K P. DeepAirNet: Applying Recurrent Networks for Air Quality Prediction”,Elsevier, Procedia computer science, 2018, 132:1394-1403.