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

  • Water Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

The objective of this study is to develop soft computing models, including Generalized Regression Neural Networks (GRNN) and Multilayer Perceptron (MLP), for modeling daily dew point temperature. For the data from U.C. Riverside and Durham stations in California, USA, the best input combinations (1-, 2-, 3-, and 4-input) were identified using GRNN. The performance evaluation and scatter diagrams of GRNN indicated that the average soil Temperature (Ts) produced the best results among 1-input combinations for both stations. Adding other input variables to the best combinations improved the performance of GRNN. MLP was used to estimate daily dew point temperature using the best input combinations (1-, 2-, 3-, and 4-input) identified by GRNN. Adding other input variables to the best input combinations also improved the performance of MLP. Comparison indicated that results of GRNN were better than those of MLP for both stations. A Multiple Linear Regression Model (MLRM), one of the conventional statistical models, was also compared with GRNN and MLP, and the soft computing models were found to estimate daily dew point temperature more accurately.

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References

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • Ayyub B. M., McCuen R. H. (2003). Probability, statistics, and reliability for engineers and scientists 2nd edition, Taylor & Francis Boca Raton, F.L.

    MATH  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Demuth H. Beale M. (2000). Neural network toolbox: For use with the MATLAB user’s guide, The MathWorks, Inc.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  MATH  MathSciNet  Google Scholar 

  • Haykin S. (2009). Neural networks and learning machines 3 rd edition, Prentice Hall, N.J.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kim S. Kim H. S. (2011). “Nonlinear evapotranspiration modeling using MLP-NNM and SVM-NNM.” Evapotranspiration, INTECH, pp. 123–147.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kottegoda N. T. Rosso R. (1997). Statistics, probability, and reliability for civil and environmental engineers, McGraw-Hill, Singapore.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • McCuen R. H. (1993). Microcomputer applications in statistical hydrology, Prentice Hall, Englewood Cliffs, N.J.

    Google Scholar 

  • Merva G. E. (1975). Physioengineering principles, AVI, Westport, C.T.

    Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Simpson P. K. (1990). Artificial neural systems: Foundations, paradigms, applications and implementations, Pergamon, N.Y.

    Google Scholar 

  • 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.

    Book  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • 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).

    Article  Google Scholar 

  • Tsoukalas L. H. Uhrig R. E. (1997). Fuzzy and neural approaches in engineering, John Wiley & Sons Inc., N.Y.

    Google Scholar 

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Authors and Affiliations

  1. Dept. of Railroad and Civil Engineering, Dongyang University, Yeongju, 750-711, Korea

    Sungwon Kim & Chang-Joon Lee

  2. Dept. of Biological and Agricultural Engineering and Zachry Dept. of Civil Engineering, Texas A & M University, College Station, Texas, 77843-2117, USA

    Vijay P. Singh

  3. Dept. of Constructional Disaster Prevention Engineering, Kyungpook National University, Sangju, 742-711, Korea

    Youngmin Seo

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  1. Sungwon Kim
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  2. Vijay P. Singh
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  3. Chang-Joon Lee
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  4. Youngmin Seo
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Correspondence to Sungwon Kim.

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Kim, S., Singh, V.P., Lee, CJ. et al. Modeling the physical dynamics of daily dew point temperature using soft computing techniques. KSCE J Civ Eng 19, 1930–1940 (2015). https://doi.org/10.1007/s12205-014-1197-4

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  • DOI: https://doi.org/10.1007/s12205-014-1197-4

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