Neural models for ambient temperature modelling

In this work we show how to model ambient temperature through neural models. In particular we tried feed forward and fully recurrent architectures, trained with the back-propagation and evolutionary algorithms, to estimate the monthly average temperature and compared the results to the nearest neighbor approach. Therefore, the best neural model has been tested to get hourly estimations. We compared the outcomes to a well known tool which doesn't have such an estimation capability and results show that the proposed approach clearly outperforms the traditional ones.

[1]  X. Yao.  Evolving Artificial Neural Networks , 1999 .

[2]  Thomas A. Huld,et al.  Estimating average daytime and daily temperature profiles within Europe , 2006, Environ. Model. Softw..

[3]  Michael F. Goodchild,et al.  Geographical information systems: principles and applications. Volume 2: applications. , 1991 .

[4]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[5]  Peter E. Thornton,et al.  Generating surfaces of daily meteorological variables over large regions of complex terrain , 1997 .

[6]  W. Beckman,et al.  Estimation of degree-days and ambient temperature bin data from monthly-average temperatures , 1983 .

[7]  John O. Carter,et al.  Using spatial interpolation to construct a comprehensive archive of Australian climate data , 2001, Environ. Model. Softw..

[8]  V. K. Nema,et al.  Prediction of monthly-mean hourly relative humidity, ambient temperature, and wind velocity for India , 1998 .

[9]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[10]  E. Cantu-Paz,et al.  An empirical comparison of combinations of evolutionary algorithms and neural networks for classification problems , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[12]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[13]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[14]  Lubos Mitas,et al.  Multivariate Interpolation of Precipitation Using Regularized Spline with Tension , 2002, Trans. GIS.

[15]  Ramakrishna R. Nemani,et al.  A flexible, integrated system for generating meteorological surfaces derived from point sources across multiple geographic scales , 2005, Environ. Model. Softw..

[16]  Aleck J. Hunter.  A Climatic Model for the Prediction of Percentile Statistics for Ambient Temperature , 1981 .

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  P. Goovaerts.  Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall , 2000 .

[19]  H. Wust,et al.  A monolithic speech recognizer based on fully recurrent neural networks , 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.

[20]  Thomas Stanford,et al.  Model identification of nonlinear time variant processes via artificial neural network , 1996 .