Abstract
Regional air quality monitoring, a critical component of sustainable development is realized through various air quality observation stations established across a region. Accurate forecasting of air quality data collected from these observation stations requires the modelling of spatial–temporal patterns in the data. Deep learning algorithms, known for their ability to capture layers of abstraction, can proficiently achieve spatial–temporal modeling. However, deterministic models that produces point forecast does not consider the underlying model uncertainty during prediction and are therefore less reliable for real-time applications. Probabilistic forecasting models that forecast prediction intervals rather than point estimates can overcome this through uncertainty quantification. The objective of the proposed study is three-fold: i) develop an efficient deterministic deep learning spatial–temporal neural network named DL-STNN for spatial–temporal air quality forecasting; ii) investigate different approaches to uncertainty quantification in deep learning models and integrate some of them, such as Monte-Carlo Dropout, Ensemble Averaging, Gaussian Process Regression, Quantile Regression, and Bayesian Inference, in tandem with DL-STNN to facilitate probabilistic forecasting; and iii) evaluate the developed deterministic and probabilistic models, using a real-world Delhi air quality dataset. The evaluation results show that, among the deterministic models, DL-STNN outperforms the baselines with 39.8% more accurate predictions and performs consistently across all seasons in Delhi. Furthermore, among the DL-STNN-based tandem models that performed probabilistic forecasting, Bayesian DL-STNN proved efficient. It does 13% more accurate point forecasting and has 20% higher suitability score than the other tandem models, indicating that Bayesian inference adapts DL-STNN more reliable for real-time applications.
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The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Reference s
Abirami S, Chitra P (2019) Real Time Twitter Based Disaster Response System for Indian Scenarios. In: 2019 26th International Conference on High Performance Computing, Data and Analytics Workshop (HiPCW). IEEE, pp 82–86
Abirami S, Chitra P (2022) Regional spatio-temporal forecasting of particulate matter using autoencoder based generative adversarial network. Stoch Environ Res Risk Assess 1–22
Abirami S, Chitra P (2021) Regional air quality forecasting using spatiotemporal deep learning. J Clean Prod 283:125341
Abirami S, Chitra P, Madhumitha R, Kesavan SR (2020) Hybrid Spatio-temporal Deep Learning Framework for Particulate Matter(PM2.5) Concentration Forecasting. In: 2020 International Conference on Innovative Trends in Information Technology (ICITIIT). pp 1–6
Akbal Y, Ünlü KD (2021) A deep learning approach to model daily particular matter of Ankara: key features and forecasting. Int J Environ Sci Technol 1–17
Al-Shedivat M, Wilson AG, Saatchi Y et al (2017) Learning scalable deep kernels with recurrent structure. J Mach Learn Res 18:2850–2886
Altikat S (2021) Prediction of CO 2 emission from greenhouse to atmosphere with artificial neural networks and deep learning neural networks. Int J Environ Sci Technol 1–10
Arora N (2021) New Delhi is world’s most polluted capital for third straight year - IQAir study | Reuters. https://www.reuters.com/article/us-india-pollution-idUSKBN2B817F. Accessed 29 Nov 2021
Atencia M, Stoean R, Joya G (2020) Uncertainty quantification through dropout in time series prediction by echo state networks. Mathematics 8:1374
Atluri G, Karpatne A, Kumar V (2018) Spatio-temporal data mining: A survey of problems and methods. ACM Comput Surv 51:1–41
Benhaddi M, Ouarzazi J (2021) Multivariate time series forecasting with dilated residual convolutional neural networks for urban air quality prediction. Arab J Sci Eng 46:3423–3442
Blundell C, Cornebise J, Kavukcuoglu K, Wierstra D (2015) Weight uncertainty in neural network. In: International Conference on Machine Learning. PMLR, pp 1613–1622
Chandra Mouli V, Chitra P, Harihara Subramanian M, Abirami S (2022) A deep learning ensemble model for short-term rainfall prediction. In: 2022 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), pp 135–138. https://doi.org/10.1109/WiSPNET54241.2022.9767163
Chaudhary V, Deshbhratar A, Kumar V, Paul D (2018) Time series based LSTM model to predict air pollutant’s concentration for prominent cities in India. UDM, Aug
Chen T, Qian Z, Jing B, et al (2020a) Probabilistic Wind Speed Forecasting based on Minimal Gated Unit and Quantile Regression. In: Journal of Physics: Conference Series. IOP Publishing, p 12039
Chen Y, Kang Y, Chen Y, Wang Z (2020) Probabilistic forecasting with temporal convolutional neural network. Neurocomputing 399:491–501
Chitra P, Abirami S (2019) Smart pollution alert system using machine learning. In: Integrating the internet of things into software engineering practices. IGI Global, pp 219–235
Chitra P, Abirami S (2020) Leveraging fog computing and deep learning for building a secure individual health-based decision support system to evade air pollution. In: Security, privacy, and forensics issues in big data. IGI Global, pp 380–406
Dadhich AP, Goyal R, Dadhich PN (2018) Assessment of spatio-temporal variations in air quality of Jaipur city, Rajasthan, India. Egypt J Remote Sens Sp Sci 21:173–181
Du S, Li T, Yang Y, Horng S-J (2019) Deep Air Quality Forecasting Using Hybrid Deep Learning Framework. IEEE Trans Knowl Data Eng 1–1. https://doi.org/10.1109/tkde.2019.2954510
Express Web Desk (2016) Diwali effect: Pollution worsens, particulate matter soars in Delhi. In: The Indian Express. http://indianexpress.com/article/india/india-news-india/post-diwali-pm-shoots-up-10-times-more-than-the-safe-limit-3730200/
Feng R, Zheng H jun, Gao H et al (2019) Recurrent Neural Network and random forest for analysis and accurate forecast of atmospheric pollutants: A case study in Hangzhou, China. J Clean Prod 231:1005–1015. https://doi.org/10.1016/j.jclepro.2019.05.319
Fortunato M, Blundell C, Vinyals O (2017) Bayesian recurrent neural networks. arXiv Prepr arXiv170402798
Freeman BS, Taylor G, Gharabaghi B, Thé J (2018) Forecasting air quality time series using deep learning. J Air Waste Manage Assoc 68:866–886
Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: international conference on machine learning. PMLR, pp 1050–1059
Gu K, Qiao J, Lin W (2018) Recurrent air quality predictor based on meteorology-and pollution-related factors. IEEE Trans Ind Informatics 14:3946–3955
Hernández-Lobato JM, Adams R (2015) Probabilistic backpropagation for scalable learning of bayesian neural networks. In: International conference on machine learning. PMLR, pp 1861–1869
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9:1735–1780
Huang CJ, Kuo PH (2018) A deep cnn-lstm model for particulate matter (Pm2.5) forecasting in smart cities. Sensors (Switzerland) 18. https://doi.org/10.3390/s18072220
Kambezidis HD, Tulleken R, Amanatidis GT et al (1995) Statistical evaluation of selected air pollutants in Athens, Greece. Environmetrics 6:349–361
Kang GK, Gao JZ, Chiao S et al (2018) Air quality prediction: Big data and machine learning approaches. Int J Environ Sci Dev 9:8–16
Kersting K, Plagemann C, Pfaff P, Burgard W (2007) Most likely heteroscedastic Gaussian process regression. In: Proceedings of the 24th international conference on Machine learning. pp 393–400
Kingma DP, Salimans T, Welling M (2015) Variational dropout and the local reparameterization trick. Adv Neural Inf Process Syst 28:2575–2583
Kingma DP, Welling M (2019) An introduction to variational autoencoders. arXiv Prepr arXiv190602691
Kirch W (ed) (2008) Pearson’s Correlation Coefficient. In: Encyclopedia of Public Health. Springer Netherlands, Dordrecht, pp 1090–1091
Koenker R, Hallock KF (2001) Quantile regression. J Econ Perspect 15:143–156
Koh PW, Liang P (2017) Understanding black-box predictions via influence functions. In: International Conference on Machine Learning. PMLR, pp 1885–1894
Korattikara A, Rathod V, Murphy K, Welling M (2015) Bayesian dark knowledge. arXiv Prepr arXiv150604416
Krishnappa L, Devatha CP (2019) Machine Learning Approaches for the Estimation of Particulate Matter (PM2.5) Concentration Levels: A Case Study in the Hyderabad City, India. pp 765–774
Lee S, Shin J (2019) Hybrid model of convolutional LSTM and CNN to predict particulate matter. Int J Inf Electron Eng 9:34–38. https://doi.org/10.18178/ijiee.2019.9.1.701
Lu X, Cui X (2020) A spatiotemporal neural network modeling method for nonlinear distributed parameter systems. IEEE Trans Ind Informatics 17:1916–1926
Lu X, Zou W, Huang M (2016) A novel spatiotemporal LS-SVM method for complex distributed parameter systems with applications to curing thermal process. IEEE Trans Ind Informatics 12:1156–1165
Ma J, Ding Y, Cheng JCP et al (2019) A temporal-spatial interpolation and extrapolation method based on geographic Long Short-Term Memory neural network for PM2. 5. J Clean Prod 237:117729
Ma X, Dai Z, He Z et al (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sensors 17:818
Mahalingam U, Elangovan K, Dobhal H, et al (2019) A machine learning model for air quality prediction for smart cities. In: 2019 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). pp 452–457
Mahmoud A, Zrigui M (2021) BLSTM-API: Bi-LSTM recurrent neural network-based approach for Arabic paraphrase identification. Arab J Sci Eng 46:4163–4174
McDermott PL, Wikle CK (2017) An ensemble quadratic echo state network for non-linear spatio-temporal forecasting. Stat 6:315–330
Mokhtari I, Bechkit W, Rivano H, Yaici MR (2021) Uncertainty-aware deep learning architectures for highly dynamic air quality prediction. IEEE Access 9:14765–14778. https://doi.org/10.1109/ACCESS.2021.3052429
Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12:229–244
Niska H, Hiltunen T, Karppinen A et al (2004) Evolving the neural network model for forecasting air pollution time series. Eng Appl Artif Intell 17:159–167
Parveen N, Siddiqui L, Sarif MDN et al (2021) Industries in Delhi: Air Pollution versus Respiratory Morbidities. Process Saf Environ Prot
Qi Y, Li Q, Karimian H, Liu D (2019) A hybrid model for spatiotemporal forecasting of PM2. 5 based on graph convolutional neural network and long short-term memory. Sci Total Environ 664:1–10
Sakarkar G, Pillai S, Rao CV et al (2020) Comparative Study of Ambient Air Quality Prediction System Using Machine Learning to Predict Air Quality in Smart City. pp 175–182
Seedat N, Kanan C (2019) Towards calibrated and scalable uncertainty representations for neural networks. arXiv Prepr arXiv191100104
Seinfeld JH (1975) Air pollution: physical and chemical fundamentals. McGraw-Hill Companies
Shanthini KM, Chitra P, Abirami S et al (2021) Recommendation of product value by extracting expiry date using deep neural network. In: 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, pp 1–7
Shepard D (1968) Proceedings of the 1968 23rd ACM National conference
Shridhar K, Laumann F, Liwicki M (2018) Uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference. arXiv Prepr arXiv180605978
Soh PW, Chang JW, Huang JW (2018) Adaptive deep learning-based air quality prediction model using the most relevant spatial-temporal relations. IEEE Access 6:38186–38199. https://doi.org/10.1109/ACCESS.2018.2849820
Sriram S, Dwivedi AK, Chitra P, et al (2022) DeepComp: A Hybrid Framework for Data Compression Using Attention Coupled Autoencoder. Arab J Sci Eng 1–16
Wang HW, Li XB, Wang D et al (2020) Regional prediction of ground-level ozone using a hybrid sequence-to-sequence deep learning approach. J Clean Prod 253:1–12. https://doi.org/10.1016/j.jclepro.2019.119841
Wu W, Chen K, Qiao Y, Lu Z (2016) Probabilistic short-term wind power forecasting based on deep neural networks. In: 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS). IEEE, pp 1–8
Zhan X, Qin H, Liu Y et al (2020) Variational Bayesian neural network for ensemble flood forecasting. Water 12:2740
Zhang J, Yan J, Infield D et al (2019) Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model. Appl Energy 241:229–244
Zhang L, Na J, Zhu J et al (2021) Spatiotemporal causal convolutional network for forecasting hourly PM2. 5 concentrations in Beijing. China. Comput Geosci 155:104869
Zhao J, Deng F, Cai Y, Chen J (2019) Long short-term memory - Fully connected (LSTM-FC) neural network for PM2.5 concentration prediction. Chemosphere 220:486–492. https://doi.org/10.1016/j.chemosphere.2018.12.128
Zhou Y, Chang F-J, Chang L-C et al (2019) Explore a deep learning multi-output neural network for regional multi-step-ahead air quality forecasts. J Clean Prod 209:134–145
Zhu A, Wu Q, Cui R et al (2020) Exploring a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN. Neurocomputing 414:90–100
Zhu D, Cai C, Yang T, Zhou X (2018) A machine learning approach for air quality prediction: model regularization and optimization. Big Data Cogn Comput 2:5. https://doi.org/10.3390/bdcc2010005
Zhu Q, Chen J, Shi D et al (2020) Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction. IEEE Trans Sustain Energy 11:509–523. https://doi.org/10.1109/TSTE.2019.2897136
Zhu S, Yuan X, Xu Z et al (2019) Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast. Energy Convers Manag 198:111772
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Abirami, S., Chitra, P. Probabilistic air quality forecasting using deep learning spatial–temporal neural network. Geoinformatica 27, 199–235 (2023). https://doi.org/10.1007/s10707-022-00479-w
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DOI: https://doi.org/10.1007/s10707-022-00479-w