Open Access Open Access  Restricted Access Subscription or Fee Access

An Efficient Weather Forecasting System Using a Hybrid Neural Network SOFM–MLP with Modified LM

Dr. S. Santhosh Baboo, I. Kadar Shereef


In this paper, primarily hybrid network is illustrated, which integrates a Self-Organizing Feature Map (SOFM) and a Multilayer Perceptron Network (MLP) to understand a much better prediction system. Then, it is demonstrated that the use of appropriate features can not only reduce the number of features, but also can improve the prediction accuracy. A feature selection MLP selects significant features online while learning the prediction task. Moreover, in this proposed approach, MLP is trained using Modified Levenberg-Marquardt algorithm for better convergence and performance. The experimental results show that the proposed approach provides significant prediction result with very less error rates.


SOFM, MLP, Modified Levenberg-Marquardt, Prediction.

Full Text:



Juha Vesanto and Esa Alhoniemi, “Clustering of the Self-Organizing Map”, IEEE Transactions on Neural Networks, Vol 11, No 3, May 2000.

Serrano, C., “Self Organizing Neural Networks for Financial Diagnosis", Decision Support Systems, 1996, Vol 17, julio, pp. 227-238, Elsevier Science.

Tomas Eklund. “Assesing the feasibility of self organizing maps for data mining financial information”, ECIS 2002 • June 6–8, Gdansk, Poland.

J. Gill, B. Singh and S. Singh, “Training back propagation neural networks with genetic algorithm for weather forecasting”, 2010 8th International Symposium on Intelligent Systems and Informatics (SISY), Pp. 465 – 469, 2010

K. Ochiai, H. Suzuki, K. Shinozawa, M. Fujii and N. Sonehara, “Snowfall and rainfall forecasting from weather radar images with artificial neural networks”, Proceedings., IEEE International Conference on Neural Networks, Vol. 2, Pp. 1182 – 1187, 1995.

M. T .Hagan and M. B. Menhaj, “Training feed forward network with the Marquardt algorithm,” IEEE Trans. on Neural Net., vol. 5, no. 6, pp.989-993, 1994.

T. Kohonen, K. Torkkola, M. Shozakai, J. Kangas, and O. Venta, “Microprocessor implementation of a large vocabulary speech recognizer and phonetic typewriter for Finnish and Japanese,” in Proc. Eur. Conf. Speech Technology, Edinburg, U.K., 1987, pp. 377–380.

N. M. Nasarabadi and Y. Feng, “Vector quantization of images based on kohonen self-organizing feature maps,” Proc. IEEE Int. Conf. on Neural Networks, pp. I-101–I-108, 1988.

T. Kohonen, The Self-Organizing Maps, 2nd ed. Berlin, Germany: Springer-Verlag, 1997.

Yuan Quan and Lu Yuchang, “Research on weather forecast based on neural networks”, Proceedings of the 3rd World Congress on Intelligent Control and Automation, Vol. 2, Pp. 1069 – 1072, 2000. bin/wyowx.fcgi?TYPE=sflist&DATE=current&HOUR=current&UNITS=A&STATION=VOMM



  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.