Abstract
The widespread of air pollution due to emissions of highly detrimental concentrates on human life needs paramount attention. Devising air prediction strategy that accurately analyze air quality level through gathering of useful information allow for relevant organizations to disseminate promptly control measures. This paper proposes the use of artificial immune system (AIS) algorithms consisting of Immunos algorithms (Immunos-1, Immunos-2, Immunos-99), CLONal selection ALGorithm (CLONALG), clonal selection classification algorithm (CSCA), artificial immune recognition system algorithms (AIRS1, AIRS2, Parallel AIRS2) for air quality prediction. The fuzzy rough set selects pertinent data features summarizing interpretations of the data. Comparative simulations reveal that the Parallel AIRS2 produced superlative results to other algorithms with detection rate of 96.40%. Effective prediction performance can be generated with AIS algorithms having highest detection rates and lowest false alarm rates.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Castelli, M., Clemente, F.M., Popovič, A., Silva, S., Vanneschi, L.: A machine learning approach to predict air quality in California. Complexity 2020, 1–23 (2020)
Masmoudi, S., Elghazel, H., Taieb, D., Yazar, O., Kallel, A.: A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection. Sci. Total Environ. 715, 136991 (2020)
Iskandaryan, D., Ramos, F., Trilles, S.: Air quality prediction in smart cities using machine learning technologies based on sensor data: a review. Appl. Sci. 10, 2401 (2020)
Apte, J.S., Brauer, M., Cohen, A.J., Ezzati, M., Pope III, C.A.: Ambient PM2. 5 reduces global and regional life expectancy. Environ. Sci. Technol. Lett. 5, 546–551 (2018)
Ghazali, R., Al-Jumeily, D.: Application of pi-sigma neural networks and ridge polynomial neural networks to financial time series prediction. In: Artificial Higher Order Neural Networks for Economics and Business, pp. 271–293. IGI Global (2009)
Husaini, N., Ghazali, R., Mohd Nawi, N., Ismail, L.: Jordan pi-sigma neural network for temperature prediction. In: Kim, T.-h, Adeli, H., Robles, R.J., Balitanas, M. (eds.) UCMA 2011. CCIS, vol. 151, pp. 547–558. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20998-7_61
Mohmad Hassim, Y.M., Ghazali, R.: Using artificial bee colony to improve functional link neural network training. In: Applied Mechanics and Materials, pp. 2102–2108 (2013)
Zhu, H., Hu, J.: Air quality forecasting using SVR with quasi-linear kernel. In: 2019 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–5 (2019)
Li, T., Hua, M., Wu, X.: A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5). IEEE Access 8, 26933–26940 (2020)
Xue, H., Bai, Y., Hu, H., Xu, T., Liang, H.: A novel hybrid model based on TVIW-PSO-GSA algorithm and support vector machine for classification problems. IEEE Access 7, 27789–27801 (2019)
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. Fuzzy Syst. IEEE Trans. 17, 824–838 (2009)
Lasisi, A., et al.: Predicting crude oil price using fuzzy rough set and bio-inspired negative selection algorithm. Int. J. Swarm Intell. Res. 10, 25–37 (2019)
Carter, J.H.: The immune system as a model for pattern recognition and classification. J. Am. Med. Informatics Assoc. 7, 28–41 (2000)
Haouari, A.T., Souici-Meslati, L., Atil, F., Meslati, D.: Empirical comparison and evaluation of Artificial Immune Systems in inter-release software fault prediction. Appl. Soft Comput. 96, 106686 (2020)
Brownlee, J.: Immunos-81, the misunderstood artificial immune system. Centre for Intelligent Systems and Complex Processes (CISCP), Faculty of Information and Communication Technologies (ICT), Swinburne University of Technology (2005)
De Castro, L.N., Von Zuben, F.J.: The clonal selection algorithm with engineering applications. In: Proceedings of GECCO, pp. 36–39 (2000)
Brownlee, J.: Clonal selection theory & clonalg-the clonal selection classification algorithm (CSCA). Swinburne University of Technology 38 (2005)
Watkins, A.B., Boggess, L.C.: A resource limited artificial immune classifier. In: Proceedings of the 2002 Congress on Evolutionary Computation. CEC 2002 (Cat. No. 02TH8600), pp. 926–931 (2002)
Acknowledgement
This work is financially supported by the Research Management Office (RMC), Universiti Tun Hussein Onn Malaysia under the Multidisciplinary Research Grant, Vote No. H494.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lasisi, A., Ghazali, R., Ismail, L.H., Husaini, N.A. (2022). Deploying Fuzzy Rough Set and Artificial Immune System Algorithms for Air Quality Prediction. In: Mahyuddin, N.M., Mat Noor, N.R., Mat Sakim, H.A. (eds) Proceedings of the 11th International Conference on Robotics, Vision, Signal Processing and Power Applications. Lecture Notes in Electrical Engineering, vol 829. Springer, Singapore. https://doi.org/10.1007/978-981-16-8129-5_152
Download citation
DOI: https://doi.org/10.1007/978-981-16-8129-5_152
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-8128-8
Online ISBN: 978-981-16-8129-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)