Prediction of Discharge in a Tidal River Using Artificial Neural Networks
Publication: Journal of Hydrologic Engineering
Volume 19, Issue 8
Abstract
Discharge predictions at tidally affected river reaches are currently still a great challenge in hydrological practices. In tidal rivers, water levels are not uniquely a function of streamflow. Here, the possibility to predict discharge from water level information from gauge stations at sea and in the river is explored. A hindcast model is established for a tide-dominated lowland site along the Mahakam River (East Kalimantan, Indonesia), using an artificial neural network (ANN) model. The input data for the ANN are gradually increased, by adding new input data in each step. The results show that the inclusion of data from tide predictions at sea leads to an improved model performance. The optimized ANN-based hindcast model produces a good discharge estimation, as shown by a consistent performance during both the training and validation periods. Using this model, discharge can be predicted from astronomical tidal predictions at sea plus water level measurements from a single station at an upstream location. Alternatively, the ANN model can be used as a tool for data gap filling in a disrupted discharge time-series based on a horizontal acoustic Doppler current profiler (H-ADCP). A forecast model is developed for the same river site that is located near the city of Samarinda. To this end, water level data, predicted tide levels, and at-site historical data are considered as input for the model. The discharge time-series derived from H-ADCP data are used for calibration and validation of the multistep ahead discharge forecast model. A good performance is obtained for predictions with a forecast lead time up to two days.
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Acknowledgments
This research has been supported by the Netherlands Organization for Scientific Research, under grant number WT 76-268. The authors thank Gerald Corzo for providing the AMI and ANN scripts. Bart Vermeulen, David Vermaas, Fajar Setiawan, and Unggul Handoko are thanked for their help in data collection. The authors also thank Remko Uijlenhoet for the discussions on the draft of this paper. The four anonymous reviewers are thanked for their comments and suggestions that have helped to improve the manuscript.
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Published In
Journal of Hydrologic Engineering
Volume 19 • Issue 8 • August 2014
Copyright
© 2014 American Society of Civil Engineers.
History
Received: Aug 16, 2013
Accepted: Jan 13, 2014
Published online: Jan 15, 2014
Published in print: Aug 1, 2014
Discussion open until: Oct 27, 2014
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