Abstract
With the gradual deepening of industrialization process, a series of problems brought about by industrial pollution. Urban development has exerted tremendous pressure on the ecological environment and has resulted in a serious impact on the lives and residents. Therefore, real-time monitoring of air quality and accurate forecasting are very important for the full utilization of the environmental data index. With an original platform, this paper deeply considers the actual needs and the performance defects of users, and build the platform of system: (1) The data map function has been added. Real-time data is identified on the map, and different colors are displayed in different intervals for PM2. 5; (2) Alarm function is added. When data exceeds a certain threshold value, logos will be displayed on the map; (3) Trend analysis functions are added, and BP artificial neural network is used to predict the data of environmental data in the future. The data scrolls playback on the map over time; (4) Reconstructing the change curve of data makes the system more intuitive, simple and easy to use. Based on the theory of BP artificial neural network, the influence of temperature, humidity, PM2.5, atmospheric pressure and illumination intensity on one of five factors above are carefully analyzed respectively. And then a reliable and suitable environmental index for predicting the five factors above is obtained. Finally, the forecast results are shown on a heat map for better visualization.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bulletin on the state of China’s environment (excerpt). The environmental protection 45, 35–47 (2017)
Carnevale, C., Finzi, G., Pisoni, E., Volta, M.: Neuro-fuzzy and neural network systems for air quality control. Atmos. Environ. 43(31), 4811–4821 (2009)
Goyal, P., Kumar, A.: Air quality forecasting throught integrated model using air dispersion model and neural network, pp. 219–224. Latest advances in systems science and computational intelligence, WSEAS, WSEAS LLC pp (2012)
Han, W., Tian, Z., Huang, Z., Li, S., Jia, Y.: Bidirectional self-adaptive resampling in imbalanced big data learning. Multimed. Tools Appl. (2018)
Hou, P., Lei, Q.: Air quality monitoring system based on wireless sensor network. Industrial instrumentation and automation, pp. 110–113
Hua, Y., Chen, B., Yuan, Y., Zhu, G., Ma, J.: An influence maximization algorithm based on the mixed importance of nodes. CMC Comput. Mater. Continua 59(2), 517–531 (2019)
Kao, J.J., Huang, S.S.: Forecasts using neural network versus box-Jenkins methodology for ambient air quality monitoring data. J. Air Waste Manag. Assoc. 50(2), 219–226 (2000)
Kolehmainen, M., Martikainen, H., Ruuskanen, J.: Neural networks and periodic components used in air quality forecasting. Atmos. Environ. 35(5), 815–825 (2001)
Kumar, A., Goyal, P.: Forecasting of air quality index in delhi using neural network based on principal component analysis. Pure Appl. Geophys. 170(4), 711–722 (2013)
Kumar, A., Singh, I.P., Sud, S.K.: Energy efficient air quality monitoring system
Li, L., Zou, C., Yu, J., Wang, B.: Research progress of air quality monitoring network design at home and abroad. China Environ. Monit. 28, 57–64 (2012)
Li, L., Fu, Q.: Design of urban air quality monitoring system based on distributed wireless sensor network. Comput. Meas. Control 22, 706–708 (2014)
Li, S., Wu, X., Zhao, D., Li, A., Tian, Z., Yang, X.: An efficient dynamic ID-based remote user authentication scheme using self-certified public keys for multi-server environments. PLoS ONE 13(10), e0202657 (2018)
Li, S., Zhao, D.L., Wu, X., Tian, Z., Li, A., Wang, Z.: Functional immunization of networks based on message passing. Appl. Math. Comput. 366, 124728 (2020)
Liu, C., Guo, H., Li, Z., Gao, X., Li, S.: Coevolution of multi-game resolves social dilemma in network population. Appl. Math. Comput. 341, 402–407 (2019)
Liu, Y., Zhu, Q., Yao, D., Xu, W.: Forecasting urban air quality via a back-propagation neural network and a selection sample rule. Atmosphere 6(7), 891–907 (2015)
Ma, Y., Yang, S., Huang, Z., Hou, Y., Cui, L., Yang, D.: Hierarchical air quality monitoring system design (2015)
Qu, Z., Cao, B., Wang, X., Li, F., Xu, P., Zhang, L.: Feedback LSTM network based on attention for image description generator. CMC Comput. Mater. Continua 59(2), 575–589 (2019)
Qu, Z., Wu, S., Liu, W., Wang, X.: Analysis and improvement of steganography protocol based on bell states in noise environment. Comput. Mater. Continua 59(2), 607–624 (2019)
Rahman, N.H.A., Lee, M.H., Suhartono, L.M.T.: Artificial neural networks and fuzzy time series forecasting: an application to air quality. Qual. Quant. 49, 2633–2647 (2015)
Saad, S.M., Saad, A.R.M., Kamarudin, A.M.Y., Zakaria, A., Shakaff, A.Y.M.: Indoor air quality monitoring system using wireless sensor network (WSN) with web interface. In: 2013 International Conference on Electrical, Electronics and System Engineering (ICEESE) (2013)
Voukantsis, D., Karatzas, K., Kukkonen, J., Rsnen, T., Karppinen, A., Kolehmainen, M.: Intercomparison of air quality data using principal component analysis, and forecasting of pm10 and pm2.5 concentrations using artificial neural networks, in thessaloniki and helsinki. Science of the Total Environment 409, 1266–1276
Wu, X.: Design of air quality monitoring system based on wireless sensor network. Ph.D. thesis, Beijing university of posts and telecommunications (2014)
Xin, R.B., Jiang, Z.F., Li, N., Hou, L.J.: An air quality predictive model of Licang of Gingdao city based on BP neural network. In: Advanced Materials Research, vol. 756, pp. 3366–3371. Trans Tech Publ (2013)
Xin, R.: Air quality prediction based on genetic optimization and Bayesian normalization neural network. Ph.D. thesis, Shandong University (2013)
Yang, H., Li, S., Wu, X., Lu, H., Han, W.: A novel solutions for malicious code detection and family clustering based on machine learning. IEEE Access 7, 148853–148860 (2019)
Zhang, T., Mao, Y., Mao, J., Wang, H., Ren, P.: An air quality prediction method based on BP neural network. Inf. Commun. 72–74 (2017)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61672020, Grant U1803263, Grant 61972106, Grant U1636215, and Grant 61972105, in part by the National Key Research and Development Program of China under Grant 2019QY1406, and in part by the Key Research and Development Program of Guangdong Province under Grant 2019B010136003.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Meng, L., Li, S., Wu, X., Han, W. (2020). Framework Design of Environment Monitoring System Based on Machine Learning. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Lecture Notes in Computer Science(), vol 12240. Springer, Cham. https://doi.org/10.1007/978-3-030-57881-7_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-57881-7_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57880-0
Online ISBN: 978-3-030-57881-7
eBook Packages: Computer ScienceComputer Science (R0)