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Volumn 24, Issue , 2014, Pages 135-141

Learning machines: Rationale and application in ground-level ozone prediction

Author keywords

Algorithms; Artificial neural networks; Learning machine; Multilayer perceptron; Ozone prediction; Support vector machine

Indexed keywords

ALGORITHMS; NEURAL NETWORKS; OZONE; SUPPORT VECTOR MACHINES;

EID: 84905037329     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2014.07.008     Document Type: Article
Times cited : (28)

References (66)
  • 1
    • 0030476772 scopus 로고    scopus 로고
    • A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area
    • J. Yi, and V.R. Prybutok A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area Environ. Pollut. 92 1996 349 357
    • (1996) Environ. Pollut. , vol.92 , pp. 349-357
    • Yi, J.1    Prybutok, V.R.2
  • 2
    • 0031172117 scopus 로고    scopus 로고
    • Comparing neural networks and regression models for ozone forecasting
    • A.C. Comrie Comparing neural networks and regression models for ozone forecasting J. Air Waste Manag. Assoc. 47 1997 653 663
    • (1997) J. Air Waste Manag. Assoc. , vol.47 , pp. 653-663
    • Comrie, A.C.1
  • 3
    • 0032146239 scopus 로고    scopus 로고
    • Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences
    • M.W. Gardner, and S.R. Dorling Artificial neural networks (the multilayer perceptron) a review of applications in the atmospheric sciences Atmos. Environ. 32 1998 2627 2636
    • (1998) Atmos. Environ. , vol.32 , pp. 2627-2636
    • Gardner, M.W.1    Dorling, S.R.2
  • 4
    • 15544389367 scopus 로고    scopus 로고
    • Potential assessment of the support vector machine method in forecasting ambient air pollutant trends
    • W.Z. Lu, and W.J. Wang Potential assessment of the support vector machine method in forecasting ambient air pollutant trends Chemosphere 59 2005 693 701
    • (2005) Chemosphere , vol.59 , pp. 693-701
    • Lu, W.Z.1    Wang, W.J.2
  • 5
    • 57849159164 scopus 로고    scopus 로고
    • Neural network prediction models as a tool for air quality management in cities
    • J. Skrzypski, and E. Jach-Szakiel Neural network prediction models as a tool for air quality management in cities Environ. Prot. Eng. 34 4 2008 129 137
    • (2008) Environ. Prot. Eng. , vol.34 , Issue.4 , pp. 129-137
    • Skrzypski, J.1    Jach-Szakiel, E.2
  • 6
    • 60249092433 scopus 로고    scopus 로고
    • Forecasting of ozone episode days by cost-sensitive neural network methods
    • C.H. Tsai, L.C. Chang, and H.C. Chiang Forecasting of ozone episode days by cost-sensitive neural network methods Sci. Total Environ. 407 6 2009 2124 2135
    • (2009) Sci. Total Environ. , vol.407 , Issue.6 , pp. 2124-2135
    • Tsai, C.H.1    Chang, L.C.2    Chiang, H.C.3
  • 9
    • 76449085807 scopus 로고    scopus 로고
    • Prediction of daily maximum ground ozone concentration using support vector machine
    • A.B. Chelani Prediction of daily maximum ground ozone concentration using support vector machine Environ. Monit. Assess. 162 1-4 2010 169 176
    • (2010) Environ. Monit. Assess. , vol.162 , Issue.14 , pp. 169-176
    • Chelani, A.B.1
  • 11
    • 29544432659 scopus 로고    scopus 로고
    • Accounting seasonal nonstationarity in time series models for short-term ozone level forecast
    • S.E. Kim, and A. Kumar Accounting seasonal nonstationarity in time series models for short-term ozone level forecast Stoch. Environ. Res. Risk A 19 2005 241 248
    • (2005) Stoch. Environ. Res. Risk A , vol.19 , pp. 241-248
    • Kim, S.E.1    Kumar, A.2
  • 12
    • 77949774686 scopus 로고    scopus 로고
    • Tree-based threshold modeling for short-term forecast of daily maximum ozone level
    • S.E. Kim Tree-based threshold modeling for short-term forecast of daily maximum ozone level Stoch. Environ. Res. Risk A 24 2010 19 28
    • (2010) Stoch. Environ. Res. Risk A , vol.24 , pp. 19-28
    • Kim, S.E.1
  • 13
    • 33745903481 scopus 로고    scopus 로고
    • Extreme learning machine: Theory and applications
    • G.B. Huang, Q.Y. Zhu, and C.K. Siew Extreme learning machine: theory and applications Neurocomputing 70 2006 489 501
    • (2006) Neurocomputing , vol.70 , pp. 489-501
    • Huang, G.B.1    Zhu, Q.Y.2    Siew, C.K.3
  • 15
    • 56049098499 scopus 로고    scopus 로고
    • Sales forecasting using extreme learning machine with applications in fashion retailing
    • Z.L. Sun, T.M. Choi, K.F. Au, and Y. Yu Sales forecasting using extreme learning machine with applications in fashion retailing Decis. Support Syst. 46 2008 411 419
    • (2008) Decis. Support Syst. , vol.46 , pp. 411-419
    • Sun, Z.L.1    Choi, T.M.2    Au, K.F.3    Yu, Y.4
  • 17
    • 0036530967 scopus 로고    scopus 로고
    • DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction
    • N. Kasabov DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction IEEE Trans. Fuzzy Syst. 10 2 2002 144 154
    • (2002) IEEE Trans. Fuzzy Syst. , vol.10 , Issue.2 , pp. 144-154
    • Kasabov, N.1
  • 21
    • 84870233667 scopus 로고    scopus 로고
    • Evolving granular neural networks
    • D. Leite, P. Costa, and F. Gomide Evolving granular neural networks Neural Netw. 38 2013 1 16
    • (2013) Neural Netw. , vol.38 , pp. 1-16
    • Leite, D.1    Costa, P.2    Gomide, F.3
  • 22
    • 67650083264 scopus 로고    scopus 로고
    • Soft chemical analyzer development using adaptive least-squares support vector regression with selective pruning and variable moving window size
    • Y. Liu, N.P. Hu, H. Wang, and P. Li Soft chemical analyzer development using adaptive least-squares support vector regression with selective pruning and variable moving window size Ind. Eng. Chem. Res. 48 12 2009 5731 5741
    • (2009) Ind. Eng. Chem. Res. , vol.48 , Issue.12 , pp. 5731-5741
    • Liu, Y.1    Hu, N.P.2    Wang, H.3    Li, P.4
  • 23
    • 84863357539 scopus 로고    scopus 로고
    • Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes
    • Y. Liu, Z. Gao, P. Li, and H. Wang Just-in-time kernel learning with adaptive parameter selection for soft sensor modeling of batch processes Ind. Eng. Chem. Res. 51 11 2012 4313 4327
    • (2012) Ind. Eng. Chem. Res. , vol.51 , Issue.11 , pp. 4313-4327
    • Liu, Y.1    Gao, Z.2    Li, P.3    Wang, H.4
  • 24
    • 84879060636 scopus 로고    scopus 로고
    • Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes
    • Y. Liu, and J. Chen Integrated soft sensor using just-in-time support vector regression and probabilistic analysis for quality prediction of multi-grade processes J. Process Control 23 6 2013 793 804
    • (2013) J. Process Control , vol.23 , Issue.6 , pp. 793-804
    • Liu, Y.1    Chen, J.2
  • 25
    • 0032170668 scopus 로고    scopus 로고
    • The application of neural techniques to the modelling of time-series of atmospheric pollution data
    • G. Nunnari, A.F.M. Nucifora, and C. Randieri The application of neural techniques to the modelling of time-series of atmospheric pollution data Ecol. Model. 11 1998 187 205
    • (1998) Ecol. Model. , vol.11 , pp. 187-205
    • Nunnari, G.1    Nucifora, A.F.M.2    Randieri, C.3
  • 26
    • 0033120626 scopus 로고    scopus 로고
    • An application of artificial neural networks to the prediction of surface ozone concentrations in the United Kingdom
    • G. Spellman An application of artificial neural networks to the prediction of surface ozone concentrations in the United Kingdom Appl. Geogr. 19 1999 123 136
    • (1999) Appl. Geogr. , vol.19 , pp. 123-136
    • Spellman, G.1
  • 27
    • 0033880828 scopus 로고    scopus 로고
    • Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations
    • V.R. Prybutok, J.S. Yi, and D. Mitchell Comparison of neural network models with ARIMA and regression models for prediction of Houston's daily maximum ozone concentrations Eur. J. Oper. Res. 122 2000 31 40
    • (2000) Eur. J. Oper. Res. , vol.122 , pp. 31-40
    • Prybutok, V.R.1    Yi, J.S.2    Mitchell, D.3
  • 29
    • 0036224943 scopus 로고    scopus 로고
    • Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks
    • S.A. Abdul-Wahab, and S.M. Al-Alawi Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks Environ. Model. Softw. 17 2002 219 228
    • (2002) Environ. Model. Softw. , vol.17 , pp. 219-228
    • Abdul-Wahab, S.A.1    Al-Alawi, S.M.2
  • 31
    • 0036468601 scopus 로고    scopus 로고
    • Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia
    • P. Viotti, G. Liuti, and P. Di Genova Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia Ecol. Model. 148 2002 27 46
    • (2002) Ecol. Model. , vol.148 , pp. 27-46
    • Viotti, P.1    Liuti, G.2    Di Genova, P.3
  • 34
    • 38949194253 scopus 로고    scopus 로고
    • Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings
    • S.C. Sofuoglu Application of artificial neural networks to predict prevalence of building-related symptoms in office buildings Build. Environ. 43 6 2008 1121 1126
    • (2008) Build. Environ. , vol.43 , Issue.6 , pp. 1121-1126
    • Sofuoglu, S.C.1
  • 36
    • 0033990385 scopus 로고    scopus 로고
    • Meteorologically adjusted trends in UK daily maximum surface ozone concentrations
    • M.W. Gardner, and S.R. Dorling Meteorologically adjusted trends in UK daily maximum surface ozone concentrations Atmos. Environ. 34 2000 171 176
    • (2000) Atmos. Environ. , vol.34 , pp. 171-176
    • Gardner, M.W.1    Dorling, S.R.2
  • 38
    • 33745714336 scopus 로고    scopus 로고
    • Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network
    • H.C. Lu, J.C. Hsieh, and T.S. Chang Prediction of daily maximum ozone concentrations from meteorological conditions using a two-stage neural network Atmos. Res. 81 2006 124 139
    • (2006) Atmos. Res. , vol.81 , pp. 124-139
    • Lu, H.C.1    Hsieh, J.C.2    Chang, T.S.3
  • 39
    • 0027205884 scopus 로고
    • A scaled conjugate gradient algorithm for fast supervised learning
    • M.F. Moller A scaled conjugate gradient algorithm for fast supervised learning Neural Netw. 6 1993 525 533
    • (1993) Neural Netw. , vol.6 , pp. 525-533
    • Moller, M.F.1
  • 40
    • 0042061161 scopus 로고    scopus 로고
    • Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens
    • A. Chaloulakou, M. Saisana, and N. Spyrellisa Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens Sci. Total Environ. 313 2003 1 13
    • (2003) Sci. Total Environ. , vol.313 , pp. 1-13
    • Chaloulakou, A.1    Saisana, M.2    Spyrellisa, N.3
  • 41
    • 17644370689 scopus 로고    scopus 로고
    • Air quality prediction in Milan: Feed-forward neural networks, pruned neural networks and lazy learning
    • G. Corani Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning Ecol. Model. 185 2005 513 529
    • (2005) Ecol. Model. , vol.185 , pp. 513-529
    • Corani, G.1
  • 43
    • 0001441372 scopus 로고
    • Probable networks and plausible predictions - A review of practical Bayesian methods for supervised neural networks
    • D.J.C. MacKay Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks Netw. Comput. Neural Syst. 6 1995 469 505
    • (1995) Netw. Comput. Neural Syst. , vol.6 , pp. 469-505
    • Mackay, D.J.C.1
  • 45
    • 33644541501 scopus 로고    scopus 로고
    • Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model
    • D. Wang, and W.Z. Lu Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model Chemosphere 62 2006 1600 1611
    • (2006) Chemosphere , vol.62 , pp. 1600-1611
    • Wang, D.1    Lu, W.Z.2
  • 48
    • 0037382121 scopus 로고    scopus 로고
    • Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong
    • W.Z. Lu, H.Y. Fan, and S.M. Lo Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong Neurocomputing 51 2003 387 400
    • (2003) Neurocomputing , vol.51 , pp. 387-400
    • Lu, W.Z.1    Fan, H.Y.2    Lo, S.M.3
  • 49
    • 33748783344 scopus 로고    scopus 로고
    • Ground-level ozone prediction using multilayer perceptron trained with an innovative hybrid approach
    • D. Wang, and W.Z. Lu Ground-level ozone prediction using multilayer perceptron trained with an innovative hybrid approach Ecol. Model. 198 2006 332 340
    • (2006) Ecol. Model. , vol.198 , pp. 332-340
    • Wang, D.1    Lu, W.Z.2
  • 50
    • 29844455331 scopus 로고    scopus 로고
    • Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm
    • D. Wang, and W.Z. Lu Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm Atmos. Environ. 40 2006 913 924
    • (2006) Atmos. Environ. , vol.40 , pp. 913-924
    • Wang, D.1    Lu, W.Z.2
  • 51
    • 0036882622 scopus 로고    scopus 로고
    • A preliminary study of ozone trend and its impact on environment in Hong Kong
    • W.Z. Lu, X.K. Wang, W.J. Wang, A.Y.T. Leung, and K.K. Yuen A preliminary study of ozone trend and its impact on environment in Hong Kong Environ. Int. 28 6 2002 503 512
    • (2002) Environ. Int. , vol.28 , Issue.6 , pp. 503-512
    • Lu, W.Z.1    Wang, X.K.2    Wang, W.J.3    Leung, A.Y.T.4    Yuen, K.K.5
  • 53
    • 34249753618 scopus 로고
    • Support-vector networks
    • 10.1007/BF00994018
    • C. Cortes, and V.N. Vapnik Support-vector networks Machi. Learn. 20 3 1995 273 10.1007/BF00994018
    • (1995) Machi. Learn. , vol.20 , Issue.3 , pp. 273
    • Cortes, C.1    Vapnik, V.N.2
  • 57
    • 66449136989 scopus 로고    scopus 로고
    • Time series prediction using support vector machines: A survey
    • N.L. Sapankevych, and R. Sankar Time series prediction using support vector machines: a survey IEEE Comput. Intell. Mag. 4 2 2009 24 38
    • (2009) IEEE Comput. Intell. Mag. , vol.4 , Issue.2 , pp. 24-38
    • Sapankevych, N.L.1    Sankar, R.2
  • 58
    • 76649131245 scopus 로고    scopus 로고
    • Greek long-term energy consumption prediction using artificial neural networks
    • L. Ekonomou Greek long-term energy consumption prediction using artificial neural networks Energy 35 2 2010 512 517
    • (2010) Energy , vol.35 , Issue.2 , pp. 512-517
    • Ekonomou, L.1
  • 59
    • 78649523121 scopus 로고    scopus 로고
    • A novel interpolation method based on differential evolution-simplex algorithm optimized parameters for support vector regression
    • D.M. Zhang, W. Liu, X. Xu, and Q.A. Deng A novel interpolation method based on differential evolution-simplex algorithm optimized parameters for support vector regression Adv. Comput. Intell. 6382 2010 64 75
    • (2010) Adv. Comput. Intell. , vol.6382 , pp. 64-75
    • Zhang, D.M.1    Liu, W.2    Xu, X.3    Deng, Q.A.4
  • 60
    • 41549103711 scopus 로고    scopus 로고
    • Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme
    • W.Z. Lu, and D. Wang Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme Sci. Total Environ. 395 2008 109 116
    • (2008) Sci. Total Environ. , vol.395 , pp. 109-116
    • Lu, W.Z.1    Wang, D.2
  • 61
    • 79955578106 scopus 로고    scopus 로고
    • Artificial neural network models for daily PM10 air pollution index prediction in the urban area of Wuhan, China
    • S.J. Wu, Q. Feng, Y. Du, and X.D. Li Artificial neural network models for daily PM10 air pollution index prediction in the urban area of Wuhan, China Environ. Eng. Sci. 28 5 2011 357 363
    • (2011) Environ. Eng. Sci. , vol.28 , Issue.5 , pp. 357-363
    • Wu, S.J.1    Feng, Q.2    Du, Y.3    Li, X.D.4
  • 62
    • 38849202538 scopus 로고    scopus 로고
    • Online prediction model based on support vector machine
    • W.J. Wang, C.Q. Men, and W.Z. Lu Online prediction model based on support vector machine Neurocomputing 71 2008 550 558
    • (2008) Neurocomputing , vol.71 , pp. 550-558
    • Wang, W.J.1    Men, C.Q.2    Lu, W.Z.3
  • 63
    • 1642276856 scopus 로고    scopus 로고
    • A meta-learning method to select the kernel width in support vector regression
    • C. Soares, P.B. Brazdil, and P. Kuba A meta-learning method to select the kernel width in support vector regression Mach. Lear. 54 3 2004 195 209
    • (2004) Mach. Lear. , vol.54 , Issue.3 , pp. 195-209
    • Soares, C.1    Brazdil, P.B.2    Kuba, P.3
  • 64
    • 0345688978 scopus 로고    scopus 로고
    • Determination of the spread parameter in the Gaussian kernel for classification and regression
    • W.J. Wang, W.Z. Xu Zongben Lu, and X.Y. Zhang Determination of the spread parameter in the Gaussian kernel for classification and regression Neurocomputing 55 2003 643 663
    • (2003) Neurocomputing , vol.55 , pp. 643-663
    • Wang, W.J.1    Xu Zongben Lu, W.Z.2    Zhang, X.Y.3
  • 65
    • 67649255836 scopus 로고    scopus 로고
    • Assessing the relative importance of surface ozone influential variables in regional-scale analysis
    • W.Z. Lu, and D. Wang Assessing the relative importance of surface ozone influential variables in regional-scale analysis Atmos. Environ. 44 22 2009 3621 3629
    • (2009) Atmos. Environ. , vol.44 , Issue.22 , pp. 3621-3629
    • Lu, W.Z.1    Wang, D.2
  • 66
    • 0038201778 scopus 로고    scopus 로고
    • Prediction of maximum daily ozone level using combined neural network and statistical characteristics
    • W.J. Wang, W.Z. Lu, X.K. Wang, and A.Y.T. Leung Prediction of maximum daily ozone level using combined neural network and statistical characteristics Environ. Int. 29 2003 555 562
    • (2003) Environ. Int. , vol.29 , pp. 555-562
    • Wang, W.J.1    Lu, W.Z.2    Wang, X.K.3    Leung, A.Y.T.4


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