Abstract
As an indispensable part of the environment, air quality has gradually become a research hotspot in recent years. The intelligent identification and prediction of atmospheric pollutants are to extract and identify the internal and external characteristics of atmospheric pollutant concentrations and integrate the influencing factors to realize the effective and stable prediction of air quality. To a certain extent, it can provide substantive reference for the control and early warning of regional atmospheric pollutant concentrations. In this chapter, single data-driven and multi-data-driven prediction models are constructed based on AQI historical data and six kinds of air pollutant concentrations data. The principal components are obtained by three feature extraction methods, the hybrid prediction model is constructed under the framework of feature extraction, and the proposed models are compared and analyzed. The results show that the prediction of pollutant concentrations after effectively extracting the main characteristics of air pollution is feasible. On this basis, this chapter also puts forward the big data calculation framework of two air pollution prediction models as a reference.
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References
Bandalos DL, Finney SJ (2018) Factor analysis: Exploratory and confirmatory. Routledge, London
Cecchetto FH, Pellanda LC (2014) Construction and validation of a questionnaire on the knowledge of healthy habits and risk factors for cardiovascular disease in schoolchildren. Jornal de Pediatria 90(4):415–419
Dong H, Li M, Zhang S, Han L, Li J, Su X (2017) Short-term power load forecasting based on kernel principal component analysis and extreme learning machine. Journal of Electronic Measurement and Instrument 32:188–193
Ebrahimy S, Osareh F (2014) Design, validation, and reliability determination a citing conformity instrument at three levels: Normative, informational, and identification. Scientometrics 99(2):581–597
Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16-18):3056–3062
Huang J, Yan X (2016) Related and independent variable fault detection based on KPCA and SVDD. Journal of Process Control 39:88–99
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: A new learning scheme of feedforward neural networks. Neural Networks 2:985–990
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1-3):489–501
Jaffel I, Taouali O, Harkat MF, Messaoud H (2017) Kernel principal component analysis with reduced complexity for nonlinear dynamic process monitoring. The International Journal of Advanced Manufacturing Technology 88(9-12):3265–3279
Jiachao S, Kun L, Jianren F, Junxi Z, Qing W, Xiang G et al (2018) Rapid response model of regional air pollutant concentration based on CMAQ and feed forward neural network. Acta Scientiae Circumstantiae 38(11):4480–4489
Jolliffe IT, Cadima J (2016) Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374(2065):20150202
Kalapanidas E, Avouris N (2003) Feature selection for air quality forecasting: A genetic algorithm approach. AI Communications 16(4):235–251
Li J, Li X, Tao D (2008) KPCA for semantic object extraction in images. Pattern Recognition 41(10):3244–3250
Li, J., Shao, X., & Zhao, H. (2018a). An online method based on random forest for air pollutant concentration forecasting. in 2018 37th Chinese Control Conference (CCC) (pp. 9641–9648). IEEE.
Li Y, Jiang P, She Q, Lin G (2018b) Research on air pollutant concentration prediction method based on self-adaptive neuro-fuzzy weighted extreme learning machine. Environmental Pollution 241:1115–1127
Liu Z, Guo W, Hu J, Ma W (2017) A hybrid intelligent multi-fault detection method for rotating machinery based on RSGWPT, KPCA and Twin SVM. ISA transactions 66:249–261
Liu H, Wu H, Lv X, Ren Z, Liu M, Li Y et al (2019) An intelligent hybrid model for air pollutant concentrations forecasting: Case of Beijing in China. Sustainable Cities and Society 47:101471
Min XU, Sun HL (2011) From “digital environmental protection” to “smart environmental protection”. Administration & Technique of Environmental Monitoring 23(4):5–7
Nair SC, Satish KP, Sreedharan J, Ibrahim H (2016) Assessing health literacy in the eastern and middle-eastern cultures. BMC Public Health 16(1):831
Navi M, Meskin N, Davoodi M (2018) Sensor fault detection and isolation of an industrial gas turbine using partial adaptive KPCA. Journal of Process Control 64:37–48
Savalei V, Kolenikov S (2008) Constrained versus unconstrained estimation in structural equation modeling. Psychological Methods 13(2):150
Teixeira AR, Tomé AM, Stadlthanner K, Lang EW (2008) KPCA denoising and the pre-image problem revisited. Digital Signal Processing 18(4):568–580
Vinay A, Shekhar VS, Murthy KB, Natarajan S (2015) Face recognition using Gabor wavelet features with PCA and KPCA-a comparative study. Procedia Computer Science 57:650–659
Yang Z, Wang J (2017) A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction. Environmental Research 158:105–117
Zhang J, Ding W (2017) Prediction of air pollutants concentration based on an extreme learning machine: The case of Hong Kong. International Journal of Environmental Research and Public Health 14(2):114
Zhang Y, Li N, Zhang Y (2015) The research of China’s urban smart environmental protection management mode. Springer, Berlin
Zhao YJ, Du B, Liu BK (2013) Smart environmental protection: The new pathway for the application of the internet of things in environmental management. Applied Mechanics & Materials 411-414:2245–2250
Zhu J, Wei-Xin DU, Jun-Ying MA, Dong N, Lin WY, Deng WY (2017) Spatial changes of air pollutants distributions before and after the “coal to gas” project in Urumqi based on numerical simulation with MM5 and CALPUFF models. Arid Land Geography 40(1):165–171
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Liu, H. (2020). Prediction Models of Urban Air Quality in Smart Environment. In: Smart Cities: Big Data Prediction Methods and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-15-2837-8_8
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DOI: https://doi.org/10.1007/978-981-15-2837-8_8
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