Abstract
The observation of peak flows into river or stream system is not straight forward but complex function of hydrology and geology. Accurate suspended sediment prediction in rivers is an integral component of sustainable water resources and environmental systems modeling. Agricultural fields’ fertility decays, rivers capacity decreases and reservoirs are filled due to sedimentation. The observation of suspended sediment flows into river or stream system is not straight forward but complex function of hydrology and geology of the region. There are statistical approaches to predict the suspended sediments in rivers. Development of models based on temporal observations may improve understanding the underlying hydrological processes complex phenomena of river sedimentation. 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 daily sediment load. The application of the proposed methodology is illustrated with real data.
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Singh, R.M. (2013). Wavelet-ANN Model for River Sedimentation Predictions. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 202. Springer, India. https://doi.org/10.1007/978-81-322-1041-2_1
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DOI: https://doi.org/10.1007/978-81-322-1041-2_1
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