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Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach

by Basawaraj Gadgay, Subhash Kulkarni, Chandrasekhar B
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 39 - Number 18
Year of Publication: 2012
Authors: Basawaraj Gadgay, Subhash Kulkarni, Chandrasekhar B
10.5120/5082-7268

Basawaraj Gadgay, Subhash Kulkarni, Chandrasekhar B . Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach. International Journal of Computer Applications. 39, 18 ( February 2012), 37-45. DOI=10.5120/5082-7268

@article{ 10.5120/5082-7268,
author = { Basawaraj Gadgay, Subhash Kulkarni, Chandrasekhar B },
title = { Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach },
journal = { International Journal of Computer Applications },
issue_date = { February 2012 },
volume = { 39 },
number = { 18 },
month = { February },
year = { 2012 },
issn = { 0975-8887 },
pages = { 37-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume39/number18/5082-7268/ },
doi = { 10.5120/5082-7268 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:26:48.757615+05:30
%A Basawaraj Gadgay
%A Subhash Kulkarni
%A Chandrasekhar B
%T Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 39
%N 18
%P 37-45
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this work, seven Ensemble Artificial Neural Network (ANN) models, namely, Multilayer Perceptron Network (MLPN), Elman Recurrent Neural Network (ERNN), Radial Basis Function Network (RBFN), Hopfield Model (HFM), Ensemble Neural Network based on Variable Inputs with No Hidden Layers (ENN-V-S), Ensemble Neural Network based on Variable Inputs with Hidden Layers (ENN-V-M), Ensemble Neural Network based on Time Inputs with No Hidden Layers (ENN-T-M), are developed to predict the rainfall for one of the large cities of India i.e. Bangalore. Different network models are developed to match the predicted results with the actual data and ENN-Average is found to be the best among all. In order to test this, actual rainfall data was collected in Bangalore city for the calendar years 2007, 2008 and 2009. This data was used as training data for the ANNs and predictions were made for the year 2010. Again these predictions were compared with the actual data to verify the performance of the ANNs. In this study, it has been proved that ENN-Average model based on back propagation algorithm provide better accurate predictions than the SNN and ENN models based on other algorithms.

References
  1. ASCE., 1996. Hydrology Handbook, second edition, American Society of Civil Engineers ASCE, New York.
  2. ASCE., 2001a. Task Committee on Artificial Neural Networks in Hydrology, Artificial Neural Networks in Hydrology. I. Preliminary concepts, Journal of Hydrologic Engineering, ASCE, 52, 115-123.
  3. ASCE 2001b. Task Committee on Artificial Neural Networks in Hydrology, Artificial Neural Networks in Hydrology. II Hydrologic Applications, Journal of Hydrologic Engineering, ASCE, 52, 124-137.
  4. Ashraf, M., Loftis J. C. and Hubbard K.G., 1997. Application of Geostatisticals to Evaluate Partial Weather Station Network. Agricultural Forest Meteorology, 84: 255-271.
  5. Boots, B. N., 1986. Voronoi Thiessen Polygons, Concepts and Techniques in Modern Geography, No. 45, Geo Book, Norwich.
  6. Bras, R.L., and I. Rodriguez-Iturbe. 1985. Random Functions and Hydrology. Addison-Wesley Publishing Company, Reading Massachusetts.
  7. Bras R.L., and R. Colon. 1978. Time Averaged Areal Mean of Precipitation: Estimation and Network Design. Water Resources Research. 14(5):878-888.
  8. Bras , R.L. and I. Rodriguez-Iturbe. 1976. Network Design for the Estimation of Area Mean of Rainfall Events. Water Resources Research. 12(6):1185-1195.
  9. Daly, C., R. P. Neilson and D. L. Phillips., 1994. A Statistical Topographic Model for Mapping Climatological Precipitation over Mountainous Terrain. Journal of Applied Meteorology, 33: 140-158.
  10. Deutsch, C. V. and A. G. Journel., 1992. Geostatistical Software Library and User’s Guide, Oxford University Press, New York.
  11. Dingman, S. L., 2002. Physical Hydrology, Prentice Hall, NJ. Freeman, J. A. and Skapura, D. M., 1991. Neural Networks: Algorithms, Applications and Programming Techniques, Addison-Wesley Inc., USA, 1991.
  12. French, M. N., Krajewski, W.F., and Cuykendal, R.R., 1992. Rainfall Forecasting in Space and Time using a Neural Network, Journal of Hydrology, 137, 1-37.
  13. Goodchild, M. F., 1986. Spatial Autocorrelation, Concepts and Techniques in Modern Geography, Geo Books, Norwich
  14. Govindaraju, R. S., and A. R. Rao., 2000. Neural Networks in Hydrology, Kluwer Academic Publishers, Netherlands.
  15. Grayson, R. and B. Gunter, 2001. Spatial Patterns in Catchment Hydrology: Observations and Modeling, Cambridge University Press.
  16. Griffith, D. A., 1987. Spatial Autocorrelation: A Primer, Association of American Geographers, Washington, D. C.
  17. Haan, C. 1977. Statistical Methods in Hydrology. Iowa State University Press, Ames, Iowa.
  18. Haykin, S. 1994. Neural Networks: A Comprehensive Foundation, Macmillan Publishing, NY.
  19. Hodgson, M. E., 1989. Searching Methods for Rapid Grid Interpolation, Professional Geographer, 411: 51-61.
  20. Isaaks, H . E., and R. M. Srivastava., 1989. An Introduction to Applied Geostatisitics, Oxford University Press, New York
  21. Journel, A. G. and C. J. Huijbregts, 1978. Mining Geostatistics, Academic Press, New York.
  22. Krajewski, W. F., 1987. Co-kriging of Radar and Rain Gage Data, Journal of Geophysics Research, 92D8, 9571-9580.
  23. Larson, L. W., and E. L. Peck., 1974. Accuracy of Precipitation Measurements for Hydrologic Forecasting, Water Resources Research, 156, 1687 - 1696.
  24. Maier, H. R., and G. C. Dandy, 1998. The Effect of Internal Parameters and Geometry on the Performance of Back-Propagation Neural Networks: An Empirical Study. Environmental Modeling and Software. 13(2), 193-209.
  25. McCuen, R. H., 1998. Hydrologic Analysis and Design, Prentice-Hall, NJ Navone, H. D., and Ceccatto, H.A., 1994. Predicting Indian Monsoon Rainfall: A Neural Network Approach, Climate Dynamics, 10, 305-312.
  26. Osborn, H. B., K.G. Renard, and J. R. Simanton., 1979. Dense Networks to Measure Convective Rainfall in the Southwestern United States, Water Resources Research, 156 : 1701-1709.
  27. Rodriguez-Iturbe, I. and J.M. Mejia. 1974. The Design of Rainfall Network in Time and Space. Water Resources Research. 10(4):713-728.
  28. Rumelhart, D.E., and J. L. Mclelland, 1986. Parallel Distributed Processing , MIT press, Cambridge, MA.
  29. Salas, J. D.-J., 1993. Analysis and Modeling of Hydrological Time Series, Handbook of Hydrology, D. R. Maidment, ed, Mc-Graw-Hill, NY
  30. Seo, D.-J., Krajewski, W.F., Bowles, D.S. 1990a. Stochastic Interpolation of Rainfall Data from Rain Gages and Radar Using Cokriging - 1. Design of Experiments. Water Resources Research, 26(3), 469-477.
  31. Seo, D.-J., Krajewski, W.F., Bowles, D.S. 1990b. Stochastic Interpolation of Rainfall Data from Rain Gages and Radar Using Cokriging - 2. Results. Water Resources Research, 26(5), 915-924.
  32. Seo, D.-J. and J. A. Smith, 1993. Rainfall Estimation Using Rain gages and Radar: A Bayesian Approach, Journal of Stochastic Hydrology and Hydraulics, 5(1), 1-14.
  33. Seo, D.-J. 1996. Nonlinear Estimation of Spatial Distribution of Rainfall - An Indicator Cokriging Approach. Stochastic Hydrology and Hydraulics, 10,127-150.
  34. Seo, D. J., 1998. Real-time Estimation of Rainfall Fields Using Radar Rainfall and Rain Gage Data, Journal of Hydrology, 208: 37-52.
  35. Simanton, J. R., and H. B. Osborn, 1980. Reciprocal-Distance Estimate of Point Rainfall, Journal of Hydraulic Engineering Division, 106HY7
  36. Singh, V. P., and K. Chowdhury., 1986. Comparing Some Methods of Estimating Mean Areal Rainfall, Water Resources Bulletin, 222, 275-282.
  37. Shepard, D., 1968. A Two-dimensional Interpolation Function for Irregularly Spaced Data, Proceedings of the Twenty-Third National Conference of the Association for Computing Machinery, 517-524.
  38. Smith, J. A., 1993. Precipitation, chapter 3, Handbook of Hydrology, D. R. Maidment ed.. McGraw Hill, New York.
  39. Sullivan, D. O. and David J. Unwin., 2003. Geographical Information Analysis, John Wiley & Sons, Inc, NJ
Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Networks Ensemble Neural Networks Rainfall Prediction ENN Averaging

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