Modeling the physical dynamics of daily dew point temperature using soft computing techniques

KSCE Journal of Civil Engineering

Volumn 19, Issue 6, 2015, Pages 1930-1940

  Kim, Sungwon ^a   Singh, Vijay P ^b   Lee, Chang Joon ^a   Seo, Youngmin ^c  

^a Dongyang University   (South Korea)

^b Texas A and M University   (United States)

^c Kyungpook National University   (South Korea)

Author keywords

California; dew point temperature; generalized regression neural networks; multilayer perceptron; multiple linear regression

Indexed keywords

LINEAR REGRESSION; MULTILAYERS; SOFT COMPUTING;

CALIFORNIA; DEWPOINT TEMPERATURE; GENERALIZED REGRESSION NEURAL NETWORKS; MULTI LAYER PERCEPTRON; MULTIPLE LINEAR REGRESSIONS;

MULTILAYER NEURAL NETWORKS;

EID: 84939264491     PISSN: 12267988     EISSN: 19763808     Source Type: Journal    
DOI: 10.1007/s12205-014-1197-4     Document Type: Article

References (41)

1. Akaike H. (1974). “New look at statistical-model identification.” IEEE Transactions on Automatic Control, Vol. 19, No. 6, pp. 716–723, DOI: 10.1109/TAC.1974.1100705.
2. ndedition, Taylor & Francis Boca Raton, F.L.
3. Coulibaly P., Anctil F., Aravena R., Bobée B. (2001). “Artificial neural network modeling of water table depth fluctuations.” Water Resources Research, Vol. 37, No. 4, pp. 885–896, DOI: 10.1029/2000WR900368.
4. Demuth H. Beale M. (2000). Neural network toolbox: For use with the MATLAB user’s guide, The MathWorks, Inc.
5. Diab H. B. Saade J. J. (1999). “Weather prediction using fuzzy sets and inference methodology.” Journal of Intelligent and Fuzzy Systems, Vol. 7, No. 3, pp. 283–305.
6. Faraway J. Chatfield C. (1998). “Time series forecasting with neural networks: A comparative study using the airline data.” Applied Statistics, Vol. 47, No. 2, pp. 231–250, DOI: 10.1111/1467-9876.00109.
7. Fletcher R. Reeves C. M. (1964). “Function minimization by conjugate gradients.” Computer Journal, Vol. 7, No. 2, pp. 149–153, DOI: 10.1093/comjnl/7.2.149.
8. rdedition, Prentice Hall, N.J.
9. Hubbard K. G., Mahmood R., Carlson C. (2003). “Estimating daily dew point temperature for the Northern Great Plains using maximum and minimum temperature.” Agronomy Journal, Vol. 95, No. 2, pp. 323–328, DOI: 10.2134/agronj2003.3230.
10. Jain S. K., Nayak P. C., Sudheer K. P. (2008). “Models for estimating evapotranspiration using artificial neural networks, and their physical interpretation.” Hydrological Processes, Vol. 22, No. 13, pp. 2225–2234, DOI: 10.1002/hyp.6819.
11. Lupo A. R., Smith N. B., Guinan P. E., Chesser M. D. (2012). “The climatology of Missouri region dew points and the relationship to ENSO.” National Weather Digest, Vol. 36, No. 2, pp. 81–91.
12. Kim S. Kim H. S. (2008). “Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling.” Journal of Hydrology, Vol. 351, Issues 3–4, pp. 299–317, DOI: 10.1016/j.jhydrol.2007.12.014.
13. Kim S. Kim H. S. (2011). “Nonlinear evapotranspiration modeling using MLP-NNM and SVM-NNM.” Evapotranspiration, INTECH, pp. 123–147.
14. Kim S., Shiri J., Kisi O. (2012). “Pan evaporation modeling using neural computing approach for different climatic zones.” Water Resources Management, Vol. 26, No. 11, pp. 3231–3249, DOI: 10.1007/s11269-012-0069-2.
15. Kim S., Shiri J., Kisi O., Singh, V. P. (2013). “Estimating daily pan evaporation using different data-driven methods and lag-time patterns.” Water Resources Management, Vol. 27, No. 7, pp. 22672286, DOI: 10.1007/s11269-013-0287-2.
16. Kim S., Singh V. P., Seo Y., Kim, H. S. (2014). “Modeling nonlinear monthly evapotranspiration using soft computing and data reconstruction techniques.” Water Resources Management, Vol. 28, No. 1, pp. 185–206, DOI: 10.1007/s11269-013-0479-9.
17. Kimball J. S., Running S. W., Nemani, R. (1997). “An improved method for estimating surface humidity from daily minimum temperature.” Agricultural and Forest Meteorology, Vol. 85, Issues 1–2, pp. 87–98, DOI: 10.1016/S0168-1923(96)02366-0.
18. Kisi O., Kim S., Shiri J. (2013). “Estimation of dew point temperature using neuro-fuzzy and neural network techniques.” Theoretical and Applied Climatology, Vol. 114, Issues 3–4, pp. 365–373, DOI: 10.1007/s00704-013-0845-9.
19. Kottegoda N. T. Rosso R. (1997). Statistics, probability, and reliability for civil and environmental engineers, McGraw-Hill, Singapore.
20. Kyoung M. S., Kim H. S., Sivakumar B., Singh V. P., Ahn K. S. (2011). “Dynamic characteristics of monthly rainfall in the Korean Peninsula under climate change.” Stochastic Environmental Research and Risk Assessment, Vol. 25, No. 4, pp. 613–625, DOI: 10.1007/s00477-010-0425-9.
21. Mahmood R., Hubbard K. G. (2005). “Assessing bias in evapotranspiration and soil moisture estimates due to the use of modeled solar radiation and dew point temperature data.” Agricultural and Forest Meteorology, Vol. 130, Issues 1–2, pp. 71–84, DOI: 10.1016/j.agrformet.2005.02.004.
22. McCuen R. H. (1993). Microcomputer applications in statistical hydrology, Prentice Hall, Englewood Cliffs, N.J.
23. Merva G. E. (1975). Physioengineering principles, AVI, Westport, C.T.
24. Mittal G. S. Zhang J. (2003). “Artificial neural network-based psychrometric predictor.” BiosystemsEngineering, Vol. 85, No. 3, pp. 283–289, DOI: 10.1016/S1537-5110(03)00071-0.
25. Nadig K., Potter W., Hoogenboom G., McClendon R. W. (2013). “Comparison of individual and combined ANN models for prediction of air and dew point temperature.” Applied Intelligence, Vol. 39, No. 2, pp. 354–366, DOI: 10.1007/s10489-012-0417-1.
26. Panchal G., Ganatra A., Kosta Y. P., Panchal D. (2010). “Searching most efficient neural network architecture using Akaike’s Information Criterion (AIC).” International Journal of Computer Applications, Vol. 1, No. 5, pp. 41–44, DOI: 10.5120/126-242.
27. Parlange M. B. Katz R. W. (2000). “An extended version of the Richardson model for simulating daily weather variables.” Journal of Applied Meteorology, Vol. 39, No. 5, pp. 610–622, DOI: 10.1175/1520-0450-39.5.610.
28. Robinson P. J. (1998). “Monthly variations of dew point temperature in the coterminous united states.” International Journal of Climatology, Vol. 18, No. 14, pp. 1539–1556, DOI: 10.1002/(SICI)1097-0088(19981130)18:14<1539::AID-JOC326>3.0.CO;2-L.
29. Robinson P. J. (2000). “Temporal trends in United States dew point temperatures.” International Journal of Climatology, Vol. 20, No. 9, pp. 985–1002, DOI: 10.1002/1097-0088(200007)20:9<985::AID-JOC513>3.0.CO;2-W.
30. Sandstrom M. A., Lauritsen R. G., Changnon D. (2004). “A central-U.S. summer extreme dew-point climatology (1949–2000).” Physical Geography, Vol. 25, No. 3, pp. 191–207, DOI: 10.2747/0272-3646.25.3.191.
31. Shank D. B., Hoogenboom G., McClendon R. W. (2008a). “Dew point temperature prediction using artificial neural networks.” Journal of Applied Meteorology and Climatology, Vol. 47, No. 6, pp. 1757–1769, DOI: 10.1175/2007JAMC1693.1.
32. Shank D. B., McClendon R. W., Paz J., Hoogenboom G. (2008b). “Ensemble artificial neural networks for prediction of dew point temperature.” Applied Artificial Intelligence, Vol. 22, No. 6, pp. 523–542, DOI: 10.1080/08839510802226785.
33. Shiri J., Kim S., Kisi O. (2014). “Estimation of daily dew point temperature using genetic programming and neural networks approaches.” Hydrology Research, Vol. 45, No. 2, pp. 165–181, DOI: 10.2166/nh.2013.229.
34. Simpson P. K. (1990). Artificial neural systems: Foundations, paradigms, applications and implementations, Pergamon, N.Y.
35. Singh V. P., Jain S. K., Tyagi A. (2007). Risk and reliability analysis: A handbook for civil and environmental engineers, ASCE Press, Reston, V.A.
36. Sivakumar B., Jayawardena A. W., Fernando T. M. K. G. (2002). “River flow forecasting: Use of phase-space reconstruction and artificial neural networks approaches.” Journal of Hydrology, Vol. 265, pp. 225–245, DOI: 10.1016/S0022-1694(02)00112-9.
37. Specht D. F. (1991). “A general regression neural network.” IEEE Transactions on Neural Networks, Vol. 2, No. 6, pp. 568–576, DOI: 10.1109/72.97934.
38. Sudheer K. P., Gosain A. K., Rangan D. M., Saheb S. M. (2002). “Modeling evaporation using an artificial neural network algorithm.” Hydrological Processes, Vol. 16, No. 16, pp. 3189–3202, DOI: 10.1002/hyp.1096.
39. Sudheer K. P., Gosain A. K., Ramasastri K. S. (2003). “Estimating actual evapotranspiration from limited climatic data using neural computing technique.” ASCE Journal of Irrigation and Drainage Engineering, Vol. 129, Issue 3, pp. 214–218, DOI: 10.1061/(ASCE)0733-9437(2003)129:3(214).
40. Tokar A. S. Johnson P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” ASCE Journal of Hydrologic Engineering, Vol. 4, Issue 3, pp. 232–239, DOI: 10.1061/(ASCE)1084-0699(1999)4:3(232).
41. Tsoukalas L. H. Uhrig R. E. (1997). Fuzzy and neural approaches in engineering, John Wiley & Sons Inc., N.Y.