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Authors: Maya Rajnarayn Ray and Arup Kumar Sarma

Affiliation: Indian Institute of Technology, India

ISBN: 978-989-8425-84-3

Keyword(s): Synthetic streamflow, Artificial neural network, Input parameters, Time step discretization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Methodologies and Methods ; Neural Based Data Mining and Complex Information Processing ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Supervised and Unsupervised Learning ; Theory and Methods

Abstract: Streamflow time series is gaining importance in planning, management and operation of water resources system day by day. In order to plan a system in an optimal way, especially when sufficient historical data are not available, the only choice left is to generate synthetic streamflow. Artificial Neural Network (ANN) has been successfully used in the past for streamflow forecasting and monthly synthetic streamflow generation. The capability of ANN to generate synthetic series of river discharge averaged over different time steps with limited data has been investigated in the present study. While an ANN model with certain input parameters can generate a monthly averaged streamflow series efficiently, it fails to generate a series of smaller time steps with the same accuracy. The scope of improving efficiency of ANN in generating synthetic streamflow by using different combinations of input data has been analyzed. The developed models have been assessed through their application in the r iver Subansiri in India. Efficiency of the ANN models has been evaluated by comparing ANN generated series with the historical series and the series generated by Thomas-Fiering model on the basis of three statistical parameters- periodical mean, periodical standard deviation and skewness of the series. The results reveal that the periodical mean of the series generated by both Thomas –Fiering and ANN models is in good agreement with that of the historical series. However, periodical standard deviation and skewness coefficient of the series generated by Thomas–Fiering model are inferior to that of the series generated by ANN. (More)

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Paper citation in several formats:
Rajnarayn Ray, M. and Kumar Sarma, A. (2011). IMPORTANCE OF INPUT PARAMETER SELECTION FOR SYNTHETIC STREAMFLOW GENERATION OF DIFFERENT TIME STEP USING ANN TECHNIQUES.In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011) ISBN 978-989-8425-84-3, pages 211-217. DOI: 10.5220/0003681802110217

@conference{ncta11,
author={Maya Rajnarayn Ray. and Arup Kumar Sarma.},
title={IMPORTANCE OF INPUT PARAMETER SELECTION FOR SYNTHETIC STREAMFLOW GENERATION OF DIFFERENT TIME STEP USING ANN TECHNIQUES},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)},
year={2011},
pages={211-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003681802110217},
isbn={978-989-8425-84-3},
}

TY - CONF

JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2011)
TI - IMPORTANCE OF INPUT PARAMETER SELECTION FOR SYNTHETIC STREAMFLOW GENERATION OF DIFFERENT TIME STEP USING ANN TECHNIQUES
SN - 978-989-8425-84-3
AU - Rajnarayn Ray, M.
AU - Kumar Sarma, A.
PY - 2011
SP - 211
EP - 217
DO - 10.5220/0003681802110217

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