Extreme learning machine based prediction of daily dew point temperature

Computers and Electronics in Agriculture

Volumn 117, Issue , 2015, Pages 214-225

  Mohammadi, Kasra ^a   Shamshirband, Shahaboddin ^b   Motamedi, Shervin ^b   Petković, Dalibor ^c   Hashim, Roslan ^b   Gocic, Milan ^c  

^a UNIVERSITY OF KASHAN   (Iran)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c UNIVERSITY OF NI   (Serbia)

Author keywords

Dew point temperature; Extreme learning machine (ELM); Prediction

Indexed keywords

BALLOONS; FORECASTING; KNOWLEDGE ACQUISITION; MEAN SQUARE ERROR; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

CASE-STUDIES; CLIMATE CONDITION; CORRELATION COEFFICIENT; DEWPOINT TEMPERATURE; EXTREME LEARNING MACHINE; PREDICTION CAPABILITY; ROOT MEAN SQUARE ERRORS; WEATHER DATA;

LEARNING SYSTEMS;

ARTIFICIAL NEURAL NETWORK; ATMOSPHERIC MODELING; CLIMATE CONDITIONS; DEW POINT; NUMERICAL MODEL; PREDICTION; RESEARCH PROGRAM; SUPPORT VECTOR MACHINE; TEMPERATURE PROFILE;

BANDAR ABBAS; HORMOZGAN; IRAN;

EID: 84940047085     PISSN: 01681699     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compag.2015.08.008     Document Type: Article

References (63)

1. Agam N., Berliner P.R. Dew formation and water vapor absorption in semi-arid environments - a review. J. Arid Environ. 2006, 65(4):572-590.
2. Annema, A.J., Hoen, K., Wallinga, H., 1994. Precision requirements for single-layer feedforward neural networks. In: Fourth International Conference on Microelectronics for Neural Networks and Fuzzy Systems. pp. 145-151.
3. Asadi S., Hadavandi E., Mehmanpazir F., Nakhostin M.M. Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction. Knowl.-Based Syst. 2012, 35:245-258.
4. Asefa T., Kemblowski M., McKee M., Khalil A. Multi-time scale stream flow predictions: the support vector machines approach. J. Hydrol. 2006, 318:7-16.
5. Aziz A., Wong K. Neural-network approach to the determination of aquifer parameters. Ground Water GRWAAP 1992, 30:164-166.
6. Balkhair K. Aquifer parameters determination for large diameter wells using neural network approach. J. Hydrol. 2002, 265:118-128.
7. Bartlett P.L. The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inform. Theory 1998, 44(2):525-536.
8. Chau K. Reliability and performance-based design by artificial neural network. Adv. Eng. Softw. 2007, 38:145-149.
9. Drezner T.D. An analysis of winter temperature and dew point under the canopy of a common Sonoran Desert nurse and the implications for positive plant interactions. J. Arid Environ. 2007, 69(4):554-568.
10. Emmel C., Knippertz P., Schulz O. Climatology of convective density currents in the southern foothills of the Atlas Mountains. J. Geophys. Res. 2010, 115(D11). 10.1029/2009JD012863.
11. Feld S.I., Cristea N.C., Lundquist J.D. Representing atmospheric moisture content along mountain slopes: examination using distributed sensors in the Sierra Nevada, California. Water Resour. Res. 2013, 49(7):4424-4441.
12. Ghouti L., Sheltami T.R., Alutaibi K.S. Mobility prediction in mobile ad hoc networks using extreme learning machines. Proc. Comput. Sci. 2013, 19:305-312.
13. Hassoun M. Fundamentals of Artificial Neural Networks 2003, A Bradford Book.
14. Haykin S.O. Neural Networks and Learning Machines 2008, Prentice Hall.
15. Huang, G.B., Zhu, Q.Y., Siew, C.K., 2004. Extreme learning machine: a new learning scheme of feedforward neural networks. In: International Joint Conference on Neural Networks. vol. 2, pp. 985-990.
16. Huang G.B., Zhu Q.Y., Siew C.K. Extreme learning machine: theory and applications. Neurocomputing 2006, 70:489-501.
17. Huang G.B., Chen L., Siew C.K. Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans. Neural Networks 2006, 17:879-892.
18. Huang G., Huang G.B., Song S., You K. Trends in extreme learning machines: a review. Neural Networks 2015, 61:32-48.
19. Hubbard K.G., Mahmood R., Carlson C. Estimating daily dew point temperature for the northern Great Plains using maximum and minimum temperature. Agron. J. 2003, 95(2):323-328.
20. Ji Y., Sun S. Multitask multiclass support vector machines: model and experiments. Pattern Recogn. 2013, 46(3):914-924.
21. Jiang Y. Estimation of monthly mean daily diffuse radiation in China. Appl. Energy 2009, 86:1458-1464.
22. Katul G.G., Oren R., Manzoni S., Higgins C., Parlange M.B. Evapotranspiration: a process driving mass transport and energy exchange in the soil-plant-atmosphere-climate system. Rev. Geophys. 2012, 50(3). 10.1029/2011RG000366.
23. Kesteven J.L., Hutchinson M.F. Spatial modelling of climatic variables on a continental scale. Proceedings of the Third International Conference/Workshop on Integrating GIS and Environmental Modeling 1996, NCGIA, Santa Barbara, California.
24. Khorasanizadeh H., Mohammadi K., Mostafaeipour A. Establishing a diffuse solar radiation model for determining the optimum tilt angle of solar surfaces in Tabass, Iran. Energy Convers. Manage. 2014, 78:805-814.
25. Kim S., Singh V.P., Lee C.J., Seo Y. Modeling the physical dynamics of daily dew point temperature using soft computing techniques. KSCE J. Civil Eng. 2014, 10.1007/s12205-014-1197-4.
26. Kimball J.S., Running S.W., Nemani R. An improved method for estimating surface humidity from daily minimum temperature. Agric. Forest Meteorol. 1997, 85:87-98.
27. Lawrence M.G. The relationship between relative humidity and the dewpoint temperature in moist air: a simple conversion and applications. Bull. Am. Meteorol. Soc. 2005, 86:225-233.
28. Lee S.-W., Verri A. Support Vector Machines for Computer Vision and Pattern Recognition 2003, World Scientific.
29. Lekouch I., Lekouch K., Muselli M., Mongruel A., Kabbachi B., Beysens D. Rooftop dew, fog and rain collection in southwest Morocco and predictive dew modeling using neural networks. J. Hydrol. 2012, 448-449:60-72.
30. Liang N.Y., Huang G.B., Rong H.J., Saratchandran P., Sundararajan N. A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans. Neural Networks 2006, 17:1411-1423.
31. Lu W.Z., Wang W.J. Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. Chemosphere 2005, 59:693-701.
32. Mahmood R., Hubbard K.G. Assessing bias in evapotranspiration and soil moisture estimates due to the use of modeled solar radiation and dew point temperature data. Agric. Forest Meteorol. 2005, 130(1-2):71-84.
33. Millán H., Ghanbarian-Alavijeh B., García-Fornaris I. Nonlinear dynamics of mean daily temperature and dewpoint time series at Babolsar, Iran, 1961-2005. Atmos. Res. 2010, 98:89-101.
34. Mirzaee H. Long-term prediction of chaotic time series with multi-step prediction horizons by a neural network with Levenberg-Marquardt learning algorithm. Chaos, Solitons Fractals 2009, 41(4):1975-1979.
35. Mohammadi K., Shamshirband S., Yee P.L., Petković D., Zamani M., Ch S. Predicting the wind power density based upon extreme learning machine. Energy 2015, 86:232-239.
36. Mohammadi K., Shamshirband S., Danesh A.S., Abdullah M.S., Zamani M. Temperature-based estimation of global solar radiation using soft computing methodologies. Theor. Appl. Climatol. 2015, 10.1007/s00704-015-1487-x.
37. Nadig K., Potter W., Hoogenboom G., McClendon R. Comparison of individual and combined ANN models for prediction of air and dew point temperature. Appl. Intell. 2013, 39(2):354-366.
38. Nguyen-Truong H.T., Le H.M. An implementation of the Levenberg-Marquardt algorithm for simultaneous-energy-gradient fitting using two-layer feed-forward neural networks. Chem. Phys. Lett. 2015, 629:40-45.
39. Nian R., He B., Zheng B., Heeswijk M.V., Yu Q., Miche Y., et al. Extreme learning machine towards dynamic model hypothesis in fish ethology research. Neurocomputing 2014, 128:273-284.
40. Parlange M.B., Katz R.W. An extended version of the Richardson model for simulating daily weather variables. J. Appl. Meteorol. 2000, 39(5):610-622.
41. Rajasekaran S., Gayathri S., Lee T.L. Support vector regression methodology for storm surge predictions. Ocean Eng. 2008, 35(16):1578-1587.
42. Samat A., Du P., Liu S., Li J., Cheng L. Ensemble extreme learning machines for hyperspectral image classification. IEEE J. Sel. Top. Appl. Earth Observations Remote Sens. 2014, 7(4):1060-1069.
43. Sarkar M. A new theoretical formulation of dew point temperatures applicable for comfort air-cooling systems. Energy Build. 2015, 86:243-256.
44. Schalkoff R.J. Artificial Neural Networks 1997, McGraw-Hill Higher Education.
45. Shamshirband S., Mohammadi K., Tong C.W., Petkovic D., Porcu E., Mostafaeipour A., Ch S., Sedaghat A. Application of extreme learning machine for estimation of wind speed distribution. Clim. Dyn. 2015, 10.1007/s00382-015-2682-2.
46. Shank D.B., Hoogenboom G., McClendon R.W. Dew point temperature prediction using artificial neural networks. J. Appl. Meteorol. Climatol. 2008, 47:1757-1769.
47. Shiri J., Kim S., Kisi O. Estimation of daily dew point temperature using genetic programming and neural networks approaches. Hydrol. Res. 2014, 45(2):165-181.
48. Singh R., Balasundaram S. Application of extreme learning machine method for time series analysis. Int. J. Intell. Technol. 2007, 2:256-262.
49. Snyder R.L., Melo-Abreu J.P. Frost Protection: Fundamentals, Practice and Economics 2005, vol. 1. Food and Agricultural Organization of the United Nations, Rome.
50. Sudheer C., Mathur S. Particle swarm optimization trained neural network for aquifer parameter estimation. KSCE J. Civil Eng. 2012, 16:298-307.
51. Sun S. A survey of multi-view machine learning. Neural Comput. Appl. 2013, 23(7-8):2031-2038.
52. Sztandera L. Computational Intelligence in Business Analytics: Concepts: Methods, and Tools for Big Data Applications 2014, FT Press.
53. Tavares L.D., Saldanha R.R., Vieira D.A.G. Extreme learning machine with parallel layer perceptrons. Neurocomputing 2015, 166:164-171.
54. Vapnik V.N., Vapnik V. Statistical Learning Theory 1998, vol. 2. Wiley, New York.
55. Vapnik V. The Nature of Statistical Learning Theory 2000, Springer.
56. Wang X., Han M. Online sequential extreme learning machine with kernels for nonstationary time series prediction. Neurocomputing 2014, 145:90-97.
57. Wang D.D., Wang R., Yan H. Fast prediction of protein-protein interaction sites based on extreme learning machines. Neurocomputing 2014, 128:258-266.
58. Wong P.K., Wong K.I., Vong C.M., Cheung C.S. Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew. Energy 2015, 74:640-647.
59. Wu K.P., Wang S.D. Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recogn. 2009, 42(5):710-717.
60. Yang H., Huang K., King I., Lyu M.R. Localized support vector regression for time series prediction. Neurocomputing 2009, 72(10):2659-2669.
61. Yu Q., Miche Y., Séverin E., Lendasse A. Bankruptcy prediction using extreme learning machine and financial expertise. Neurocomputing 2014, 128:296-302.
62. Zhao Z., Li P., Xu X. Forecasting model of coal mine water inrush based on extreme learning machine. Appl. Math. Inform. Sci. 2013, 7:1243-1250.
63. Zounemat-Kermani M. Hourly predictive Levenberg-Marquardt ANN and multi linear regression models for predicting of dew point temperature. Meteorol. Atmos. Phys. 2012, 117(3-4):181-192.