Artificial Neural Network Modeling of Relative Humidity and Air Temperature Spatial and Temporal Distributions Over Complex Terrains

  • Kostas Philippopoulos
  • Despina Deligiorgi
  • Georgios Kouroupetroglou
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 318)

Abstract

In this work we present a methodological approach of applying Artificial Neural Networks (ANN) for modeling of both the air temperature (AT) and relative humidity (RH) spatial and temporal distributions over complex terrains. A number of implementation issues are discussed, along with their relative advantages and limitations. Moreover, after the introduction of a set of metrics, the accuracy of the evaluation of ANN based spatial and time series AT and RH modeling in the case of a specific region is examined, by applying a number of alternative feed forward ANN topologies. The Levenberg-Marquardt back propagation algorithm was used for the ANNs training in the temporal forecasting of AT and RH, with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. The Radial Basis Function and the Multilayer Perceptrons non-linear Feed Forward ANNs schemes are compared for the spatial estimation of AT and RH. We found that the spatial and temporal AT and RH variability over complex terrains can be modeled efficiently by ANNs.

Keywords

Artificial neural networks Relative humidity modeling Air temperature modeling Spatial interpolation Time-series forecasting 

References

  1. 1.
    Price, D.T., McKenney, D.W., Nalder, I.A., Hutchinson, M.F., Kesteven, J.L.: A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric. For. Meteorol. 101(2–3), 81–94 (2000). doi:10.1016/S0168-1923(99)00169-0 CrossRefGoogle Scholar
  2. 2.
    Chai, H., Cheng, W., Zhou, C., Chen, X., Ma, X., Zhao, S.: Analysis and comparison of spatial interpolation methods for temperature data in Xinjiang Uygur autonomous region, China. Nat. Sci. 3(12), 999–1010 (2011). doi:10.4236/ns.2011.312125 Google Scholar
  3. 3.
    Deligiorgi, D., Philippopoulos, K.: Spatial interpolation methodologies in urban air pollution modeling: application for the greater area of metropolitan Athens, Greece. In: Nejadkoorki, F. (ed.) Advanced Air Pollution. InTech Publishers, Rijeka (2011). doi:10.5772/17734
  4. 4.
    Deligiorgi, D., Philippopoulos, K., Kouroupetroglou, G.: Artificial neural network based methodologies for the estimation of wind speed. In: Cavallaro, F.F. (ed.) Assessment and Simulation Tools for Sustainable Energy Systems. Springer, Berlin (2013)Google Scholar
  5. 5.
    Snell, S., Gopal, S., Kaufmann, R.: Spatial Interpolation of surface air temperatures using artificial neural networks: evaluating their use for downscaling GCMs. J. Clim. 13(5), 886–895 (2000). doi:10.1175/1520-0442(2000)013<0886:SIOSAT>2.0.CO;2 CrossRefGoogle Scholar
  6. 6.
    Chronopoulos, K., Tsiros, I., Dimopoulos, I., Alvertos, N.: An application of artificial neural network models to estimate air temperature data in areas with sparse network of meteorological stations. J. Environ. Sci. Health Part A: Tox./Hazard. Subst. Environ. Eng. 43(14), 1752–1757 (2008). doi:10.1080/10934520802507621 CrossRefGoogle Scholar
  7. 7.
    Tasadduq, I., Rehman, S., Bubshait, K.: Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew. Energy 25(4), 545–554 (2002). doi:10.1016/S0960-1481(01)00082-9 CrossRefGoogle Scholar
  8. 8.
    Dombayc, O., Golcu, M.: Daily means ambient temperature prediction using artificial neural network method: a case study of Turkey. Renew. Energy 34(3), 1158–1161 (2009). doi:10.1016/j.renene.2008.07.007 CrossRefGoogle Scholar
  9. 9.
    Smith, B., Hoogenboom, G., McClendon, R.: Artificial neural networks for automated year-round temperature prediction. Comput. Electron. Agric. 68(1), 52–61 (2009). doi:10.1016/j.compag. 2009.04.003 CrossRefGoogle Scholar
  10. 10.
    Mustafaraj, G., Lowry, G., Chen, J.: Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural network models for an open office. Energy Build. 43(6), 1452–1460 (2011). doi:10.1016/j.enbuild.2011.02.007 CrossRefGoogle Scholar
  11. 11.
    Mihalakakou, G., Flocas, H., Santamouris, M., Helmis, C.: Application of neural networks to the simulation of the heat island over Athens, Greece, using synoptic types as a predictor. J. Appl. Meteorol. 41(5), 519–527 (2002). doi:10.1175/1520-0450(2002)041<519:AONNTT>2.0.CO;2 CrossRefGoogle Scholar
  12. 12.
    Fausett, L.V.: Fundamentals Neural Networks: Architecture, Algorithms, and Applications. Prentice-Hall Inc., New Jersey (1994)Google Scholar
  13. 13.
    Bishop, C.M.: Neural Networks For Pattern Recognition. Oxford University Press, Cambridge (1995)Google Scholar
  14. 14.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (1996). doi:10.1109/2.485891 CrossRefGoogle Scholar
  15. 15.
    Zhang, G.P., Patuwo, E., Hu, M.: Forecasting with artificial neural networks: the state of the art. Int. J. Forecast. 14(1), 35–62 (1998). doi:10.1016/S0169-2070(97)00044-7 CrossRefGoogle Scholar
  16. 16.
    Cybenco, G.: Approximation by superposition of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989). doi:10.1007/BF02551274 CrossRefGoogle Scholar
  17. 17.
    Hornik, K., Stinchcombe, M., White, H.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989). doi:10.1016/0893-6080(89)90020-8 CrossRefGoogle Scholar
  18. 18.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323, 533–536 (1986). doi:10.1038/323533a0 CrossRefGoogle Scholar
  19. 19.
    Yu, H., Wilamowski, B.M.: Levenberg-Marquardt training. In: Wilamowski, B.M., Irwin, J.D. (eds.) Industrial Electronics Handbook, 2nd edn. CRC Press, Boca Raton (2011)Google Scholar
  20. 20.
    Fox, D.G.: Judging air quality model performance. Bull. Am. Meteorol. Soc. 62(5), 599–609 (1981). doi:10.1175/1520-0477(1981)062<0599:JAQMP>2.0.CO;2 CrossRefGoogle Scholar
  21. 21.
    Willmott, C.J.: Some comments on the evaluation of model performance. Bull. Am. Meteorol. Soc. 63(11), 1309–1313 (1982). doi:10.1175/1520-0477(1982)063<1309:SCOTEO>2.0.CO;2 CrossRefGoogle Scholar
  22. 22.
    Koletsis, I., Lagouvardos, K., Kotroni, V., Bartzokas, A.: The interaction of northern wind flow with the complex topography of Crete island-Part 1: observational study. Nat. Hazards Earth Syst. Sci. 9, 1845–1855 (2009). doi:10.5194/nhess-9-1845-2009 CrossRefGoogle Scholar
  23. 23.
    Koletsis, I., Lagouvardos, K., Kotroni, V., Bartzokas, A.: The interaction of northern wind flow with the complex topography of Crete island-Part 2: numerical study. Nat. Hazards Earth Syst. Sci. 10, 1115–1127 (2010). doi:10.5194/nhess-10-1115-2010 CrossRefGoogle Scholar
  24. 24.
    Kotroni, V., Lagouvardos, K., Lalas, D.: The effect of the island of Crete on the etesian winds over the Aegean sea. Q. J. R. Meteorol. Soc. 127(576), 1917–1937 (2001). doi:10.1002/qj.49712757604 CrossRefGoogle Scholar
  25. 25.
    Deligiorgi, D., Kolokotsa, D., Papakostas, T., Mantou, E.: Analysis of the wind field at the broader area of Chania, Crete. In: 3rd IASME/WSEAS International Conference on Energy, Environment and Sustainable Development, pp. 270–275. Agios Nikolaos, Crete: World Scientific and Engineering Academy and Society Press (2007). Retrieved from:http://www.wseas.us/e-rary/conferences/2007creteeeesd/papers/562-194.pdf
  26. 26.
    Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos. Environ. 32(14–15), 2627–2636 (1998). doi:10.1016/S1352-2310(97)00447-0 CrossRefGoogle Scholar
  27. 27.
    Heaton, J.: Introduction to Neural Networks with Java. Heaton Research Inc, Chesterfield (2005)Google Scholar
  28. 28.
    Powel, M.J.D.: Radial basis functions for multivariable interpolation: a review. In: Mason, J.C., Cox, M.G. (eds.) Algorithms for Approximation. Clarendon Press, Oxford (1987)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kostas Philippopoulos
    • 1
  • Despina Deligiorgi
    • 1
  • Georgios Kouroupetroglou
    • 2
  1. 1.Division of Environmental Physics and Meteorology, Department of PhysicsNational and Kapodistrian University of AthensAthensGreece
  2. 2.Division of Communication and Signal Processing, Department of Informatics and TelecommunicationsNational and Kapodistrian University of AthensAthensGreece

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