Abstract
The techniques pertaining to soft computing are the base for commencement, model and organization of intelligent systems in order to offer more perfect, economical and realistic solution which has minimal complexity levels. The concept of intelligent systems has for long supported objectives on sustainability, improvisation of efficiency and symbolized various kinds of activities like creation of jobs, earning profits, providing services and improvement in capacities in ICT. The applications based on this system are widespread in the technological market because of massive development in grid, cloud, mobile and big data applications and its corresponding connectivity advantages. ICT triggers data scientists to satisfy technical-based demands of intelligent systems around data analytics and big data applications. The major features of big data like that of reservation of designs on information and knowledge have offered the public undertaking an opportunity to enhance production levels, improved efficiency and its effectiveness. The main objective of this paper is to enhance the accuracy of predictive modelling using an optimized predictive modelling based on rough set (RS) and Elman neural network (ElNN). These advanced predictive models are designed on the basis of RS approach in initial stages and in later processes enhanced with the support of Elman-NN. RS has an excellent feature selection capability, and Elman-NN is the best at nonlinear system modelling. By integrating them, the proposed method can limit the input dimension and optimize the structure for ElNN modelling. This can reduce the mathematical computation complexity with the progress of predictive models. The experimental results indicate that through RS feature selection and the structure of Elman-NN, the predictive model can be simplified significantly with enhanced model performance. The predictive accuracy of data sets, namely air quality in Northern Taiwan, hazardous air pollutants, historical hourly weather data and US pollutants through optimization, is above 99%, and this model proves that the results of optimized predictive error are far better than those obtained by other neural networks like PCA-RBF, PCA-NN, FFNN-BP with PCA, MLR, FFNN-BP, ELM, SOM, RBF and ART2.
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References
Arampatzis T, Lygeros J, Manesis S (2005) A survey of applications of wireless sensors and wireless sensor networks. In: Proceedings of the 13th Mediterranean conference on control and automation limassol Cyprus Turkey, pp 719–724
Azid A, Juahir H, Toriman M, Kamarudin M, Saudi A, Hasnam C, Aziz N, Azaman F, Latif M, Zainuddin S et al (2014) Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: a case study in Malaysia. Water Air Soil Pollut 225:1–14
Bougoudis I, Demertzis K, Iliadis L (2016) HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modelling in Athens. Neural Comput Appl 27:1191–1206
Chang L-S, Cho A, Park H, Nam K, Kim D, Hong J-H, Song C-K (2016) Human-model hybrid Korean air quality forecasting system. Int J Air Waste Manag Assoc 66:9
Corani G (2005) Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning. Ecol Model 185:513–529
Dos Santos EP, Von Zuben FJ (2000) Efficient second order learning algorithms for discrete-time recurrent neural networks. In: Medsker LR, Jain LC (eds) Recurrent neural networks: design and applications. CRC Press, Boca Raton, pp 47–75. ISBN 0-8493-7181-3
Fasbender D, Brasseur O, Bogaert P (2009) Bayesian data fusion for space-time prediction of air pollutants: the case of NO2 in Belgium. Atmos Environ 43:4632–4645
Ferrari S, Stengel RF (2005) Smooth function approximation using neural networks. IEEE Trans Neural Netw 6:24–38
Freeman BS, Taylor G, Gharabaghi B, The J (2018) Forecasting air quality time series using deep learning. Int J Air Waste Manag Assoc 68:8
Fu M, Wang W, Le Z, Khorram MS (2015) Prediction of particular matter concentrations by developed feed-forward neural network with rolling mechanism and gray model. Neural Comput Appl 26:1789–1797
Kumar A, Goyal P (2013) Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl Geophys 170:711–722
Kwong K (2001) Financial forecasting using neural network or machine learning techniques. Thesis of Electrical Engineering, University of Queensland
Lei L (2017) Wavelet neural network prediction method of stock price trend based on rough set attribute reduction. J Appl Soft Comput. https://doi.org/10.1016/j.asoc.2017.09.029
Paschalidou AK, Karakitsios S, Kleanthous S, Kassomenos PA (2011) Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management. Environ Sci Pollut Res 18:316–327
Pawlak Z (1982) Rough sets. Int J Inf Comput Sci 11:341–356
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer, Norwell (ISBN 0-79231472)
Pawlak Z, Skowron A (2007) Rudiments of rough sets. J Inf Sci 177:3–27
Riza LS, Janusz A, Bergmeir C, Cornelis C, Herrera F, Slezak D, Benitez JM (2014) Implementing algorithms of rough set theory and fuzzy rough set theory in the R package “RoughSets”. J Inf Sci 287:68–89
Selvi S, Chandrasekaran M (2018) Performance evaluation of mathematical predictive modelling for air quality forecasting. Clust Comput 22(Suppl 5):12481–12493
Sengur A, Turkoglu I, Ince MC (2007) Wavelet packet neural networks for texture classification. Expert Syst Appl 32:527–533
Skowron A, Rauszern C (1992) The discernibility matrices and functions in information systems. In: Słowinski R (ed) Intelligent decision support: handbook of applications and advances of rough sets theory. Kluwer, Dordrecht, pp 331–362
Smys S, Bestak R, Chen JI-Z (2019) Special issue on evolutionary computing and intelligent sustainable systems. Soft Comput 23(18):8333–8333
Tan Q, Wei Y, Wang M, Liu Y (2014) A cluster multivariate statistical method for environmental quality management. Eng Appl Artif Intell 32:1–9
Zhang J, Ding W (2017) Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. Int J Environ Res Public Health MDPI 14:114
Zhang G et al (1998) Forecasting with artificial neural networks: the state of the art. Int J Forecast 14:35–62
Zhang Q, Xie Q, Wang G (2016) A survey on rough set theory and its applications. CAAI Trans Intell Technol 1:323–333
Zhao W, Fan SJ, Guo H, Gao B, Sun JR, Chen LG (2016) Assessing the impact of local meteorological variables on surface ozone in Hong Kong during 2000–2015 using quantile and multiple line regression models. Atmos Environ 144:182–193
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Selvi, S., Chandrasekaran, M. Framework to forecast environment changes by optimized predictive modelling based on rough set and Elman neural network. Soft Comput 24, 10467–10480 (2020). https://doi.org/10.1007/s00500-019-04556-5
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DOI: https://doi.org/10.1007/s00500-019-04556-5