Abstract
The observation of peak flows into river or stream system is not straight forward but complex function of hydrology and geology of the region. There are well established statistical approach to predict the flood events with their magnitude and frequency. Development of models based on temporal observations may improve understanding the underlying hydrological processes in such complex phenomena. Present work utilized temporal patterns extracted from temporal observations of annual peak series using wavelet theory. These patterns are then utilized by an artificial neural network (ANN). The wavelet-ANN conjunction model is then able to predict the flood event comparable to statistical approach. The application of the proposed methodology is illustrated with real data. The limited performance evaluation of the methodology show potential application of the developed methodology.
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Singh, R.M. (2012). Wavelet-ANN Model for Flood Events. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_16
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DOI: https://doi.org/10.1007/978-81-322-0491-6_16
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-0490-9
Online ISBN: 978-81-322-0491-6
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