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Superensembling of Artificial Neural Network Models for Investigating the Effect of Polar Sea Ice on Indian summer Monsoon Rainfall
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 165 - Number 2 |
| Year of Publication: 2017 |
| Authors: Shruthy Govindan, K. C. Tripathi |
10.5120/ijca2017913800
|
Shruthy Govindan, K. C. Tripathi . Superensembling of Artificial Neural Network Models for Investigating the Effect of Polar Sea Ice on Indian summer Monsoon Rainfall. International Journal of Computer Applications. 165, 2 ( May 2017), 28-35. DOI=10.5120/ijca2017913800
Abstract
Multimodel ensembling is now a popular prediction among researchers of dynamical meteorology. The ensembling of model outputs obtained from more than one initializations reduces the errors in forecasting due to wrong initializations. Multi model ensembling reduces the errors on account of poor understanding of physical phenomenon. Apart from dynamical forecasting, statistical forecasting contributes significantly to the research of this area. It becomes a pertinent question if the ensembling reduces this error in the same light as the dynamical ensembling. To address this question we have considered Artificial Neural Network models which has become a popular statistical model among the researchers of this community. We define the Superensemble model as the model which is a model based on ensembles of many ANN models. The ensemble ANN model is a simple ensemble of more than one ANNs with different initializations. Various approaches to superensembling have been discussed. The superensemble ANN models have been used to study the impact of Antarctic Sea Ice Concentration on the Sea Surface Temperature in an Indian Ocean region. The superensemble forecast is compared with normal and ensemble forecast. The results give a mixed interpretation.
References
- Aggarwal,Rashi., Kumar,Rajendra. (2013) ‘A comprehensive review of Numerical Weather Prediction Models’, International Journal of Computer Applications,Vol.47
- Basawaraj,Gadgay., Kulkarni, S., Chandrasekhar,B. (2012) ‘ Novel Ensemble Neural Network Models for better Prediction using Variable Input Approach’, International Journal of Computer Applications,Vol.39-no.18.
- Mekanik, F. and Imteaz, M.A, (2012) ‘A Multivariate Artificial Neural Network Approach for Rainfall Forecasting: Case Study of Victoria, Australia’, Proceedings of the World Congress on Engineering and Computer Science Vol I,WCES .
- Gheyas, I. A. and Smith, L. S.(2012) ‘A novel neural network ensemble architecture for time series forecasting’, Neurocomputing, doi:10.1016/j.neucom.2011.08.005 .
- Shrivastava,Gyanesh., Karmakar,Sanjeev., Kowar, M.K., Guhathakurta, Pulak. (2012) ’Application of Artificial Neural Networks in Weather Forecasting: A Comprehensive Literature Review’, International Journal of Computer Applications ,Vol 51– No.18.
- Hansen, L.K., Salamon, P.(1990) ‘ Neural network ensembles ’, IEEE Trans Pattern Anal 12(10):993-1001.
- Hsieh, W.W.& Tang, B.(1998) ‘Applying neural networks model to prediction and data analysis in meteorology and oceanography’, Bull. Amer. Meteorol Soc.,79,pp.1855-1870.
- Maqsood,Imran., Khan,M.R. , Abaraham, A. (2004) ,‘An ensemble of neural networks for weather forecasting’, Springer-Verlag, Neural Comput & Applic ,Vol. 13 pp.112-122.
- Tripathi,K.C., Agarwal, Rashi., Hrisheekesha, P.N. (2013) , ‘Global Prediction algorithms and predictability of anomalous points in a time series’, International journal of computers & technology, Vol.10, no 9.
- Krishnamurti ,T.N and Kishtawal C.M., Larow,T. ,et, al.,(1994) , ‘ Improved weather and seasonal forecasts from multi-model superensemble ‘,Science ,pp.1548-1550
- Lynch ,P.(2008) ,‘ The Origins of Computer Weather Prediction and Climatic Modelling’. Jounal on Computational Physics ’, 227, pp.3431–3444.
- McCulloh , W.S. & Pitts ,W.(1943), ‘A logical calculus of the ideas immanent in neural nets’, Bull Math. Biophys.,5(1943) ,pp.133-137
- Palmer, T.N. and others,(2004) ,‘ Development of a European multi-model ensemble system for seasonal to inter-annual prediction (DEMETER)’ ,Bulletin of American Meteorological Society,85,pp.853-872.
- Rai, S., Pandey , A. C. , Tripathi, K. C. and Dwivedi ,S. (2008), ‘ Predictive Skill of DEMETER models for wind prediction near Madagascar’, Indian Journal of Marine Sciences, 37.
- Sahai, A. K. , Chattopadhyay ,R. and Goswami , B. N. (2008), ‘A SST based large multi-model ensemble forecasting system for Indian summer monsoon rainfall’, Geophysical Research Letters, 35, L19705, doi:10.1029/2008GL035461.
- Rai, Shailendra., Khare, N. and Pandey, A.C. (2008), ‘Antarctica sea ice variability and southeast Indian Ocean SST: possible relationship’, Indian Journal of Marine Sciences,Vol.37(1),pp.35-39.
- Bhowmik,S. K. R., Durai ,V.R(2012), ‘Development of multimodel ensemble based district level medium range rainfall’, Indian Academy of Science, 121, no. 2 , pp. 273-285.
- Yun, W.T., Stefanova ,L., Mitra, A.K., Dewar,W. and Krishnamurti ,T.N. (2004), ‘Multi-Model Synthetic superensemble Algorithm for seasonal climate prediction using DEMETER Forecasts’, Tellus, DEMETER .
- Krasnopolsky ,V.M. and Lin,Ying. (2012), ‘A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US’, Advances in Meteorology, Article ID 649450, 11 pages, 2012. doi:10.1155/2012/649450.
- Zhou ZH, Wu J, Tang W (2002), ‘Ensembling neural networks: many could be better than all’, Artificial Intelligence 137(1-2), pp. 239-263.
- Zurada, J.M.(2002), ‘Introduction to artificial neural systems’, Jaico Publishing House, Mumbai, India, pp. 30-400.
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