Abstract
One of the main environmental concerns in recent time is Air Pollution. The air pollution is caused by rapid rise in the concentration of any harmful gas. Ozone (O3), which is the most gaseous pollutants in major cities around the globe, is a major concern for the pollution. The ozone molecule (O3), outside of ozone layer, is harmful to the air quality. This paper focuses on two predictive models which are used to calculate the approximate amount of ozone gas in air. The models being, Random Forest and Multivariate Adaptive Regression Splines. By evaluating the prediction models, it was found that Multivariate Adaptive Regression Splines model has a better prediction accuracy than Random Forest as it produced better datasets. A thorough comparative study on the performances of Multivariate Adaptive Regression Splines and Random Forest was performed. Also, variable importance for each prediction model was predicted. Multivariate Adaptive Regression Splines provides the result by using less variables as compared to the other prediction model. Furthermore, Random Forest model generally takes more time, here it took 45 s more as per the evaluation for building the tree. Monitoring the different graphs produced by the models, Multivariate Adaptive Regression Splines provides the closest curve for both the train set and test set when compared. It can be concluded as the multivariate adaptive regression splines prediction model can be used as a necessary tool in predicting ozone in near future.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ozer P, Laghdaf MBOM, Lemine SOM, Gassan J (2007) Estimation of air quality degradation due to Saharan dust at Nouakchott, Mauritania, from horizontal visibility data. Water, Air, Soil Pollut 178(1–4):79
Zhang WY, Han TT, Zhao ZB, Zhang J, Wang YF (2011) The prediction of surface layer ozone concentration using an improved AR model. In: 2011 International conference of information technology, computer engineering and management sciences. Nanjing, Jiangsu, pp 72–75
Assessment for decision-makers: scientific assessment of ozone depletion: 2014, world meteorological organization, global ozone research and monitoring project—report no. 56, Geneva, Switzerland, 2014
Birgersson M, Hansson G, Franke U (2016) Data integration using machine learning. In: 2016 IEEE 20th international enterprise distributed object computing workshop (EDOCW), Vienna, Austria, pp 1–10
Roy SS, Viswanatham VM, Krishna PV (2016) Spam detection using hybrid model of rough set and decorate ensemble. Int J Comput Syst Eng 2(3):139–147
Roy SS, Viswanatham VM (2016) Classifying spam emails using artificial intelligent techniques. Int J Eng Res in Africa 22
Basu A, Roy SS, Abraham A (2015) A novel diagnostic approach based on support vector machine with linear kernel for classifying the erythemato-squamous disease. In: 2015 International conference on computing communication control and automation (ICCUBEA), pp 343–347. IEEE
Mittal D, Gaurav D, Roy SS (2015) An effective hybridized classifier for breast cancer diagnosis. In: 2015 IEEE international conference on advanced intelligent mechatronics (AIM), pp. 1026–1031. IEEE
Roy SS, Gupta, A, Sinha A, Ramesh R (2012) Cancer data investigation using variable precision Rough set with flexible classification. In: Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology, pp 472–475. ACM
Popescu-Bodorin N, Balas VE, Motoc IM (2011) Iris codes classification using discriminant and witness directions. arXiv preprint arXiv:1110.6483
Roy SS, Viswanatham VM, Krishna PV, Saraf N, Gupta A, Mishra R (2013) Applicability of rough set technique for data investigation and optimization of intrusion detection system. In: International conference on heterogeneous networking for quality, reliability, security and robustness. Springer Berlin Heidelberg, pp. 479–484
Zhang C, Yuan D (2015) Fast fine-grained air quality index level prediction using random forest algorithm on cluster computing of spark. In: 2015 IEEE 12th International conference on ubiquitous intelligence and computing and 2015. IEEE 12th international conference on autonomic and trusted computing and 2015. IEEE 15th international conference on scalable computing and communications and its associated workshops (UIC-ATC-ScalCom), Beijing, pp 929–934
De Vito S, Massera E, Piga M, Martinotto L, Di Francia G (2008) On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario. Sens Actuators B: Chem 129(2):750–757
De Vito S, Piga M, Martinotto L, Di Francia G (2009) CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization. Sens Actuators B: Chem 143(1):182–191. ISSN 0925-4005
Hui TS, Rahman, SA, Labadin J (2013) Comparison between multiple regression and multivariate adaptive regression splines for predicting CO2 emissions. In: 2013 8th international conference on asean countries, information technology in Asia (CITA), Kota Samarahan, pp 1–5
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2019 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Srinivas, A.S., Somula, R., Govinda, K., Manivannan, S.S. (2019). Predicting Ozone Layer Concentration Using Machine Learning Techniques. In: Social Network Forensics, Cyber Security, and Machine Learning. SpringerBriefs in Applied Sciences and Technology(). Springer, Singapore. https://doi.org/10.1007/978-981-13-1456-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-13-1456-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1455-1
Online ISBN: 978-981-13-1456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)