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Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment

Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment In this study, an attempt has been made to develop an ANN model to estimate runoff from a snow covered catchment of eastern Himalaya using feed-forward back-propagation algorithm with Levenberg–Marquardt optimization technique. The ANN model was programmed in C++ whereas a user-friendly GUI was developed in VB. The effects of past days rainfall and present day temperature data was observed on the performance of the selected ANN architecture in modelling snowmelt and monsoon season runoff. For this purpose, 8 years’ (2003–2010) daily data (rainfall, temperature, and discharge) were collected from CWC which were again divided into two parts (2003–2008 and 2009–2010) for training and testing of the ANN model, respectively. Initially it was found that the network can produce acceptable results with only rainfall data as input, but it needs at least past 3 days rainfall data to account for the antecedent moisture condition of the catchment. Networks 4-16-16-1 (with past 3 days rainfall) and 6-18-18-18-1 (with past 5 days rainfall) resulted modelling efficiency of 79.38 and 82.06% in training and 55.13 and 61.06% in validation, respectively. However, addition of present day temperature data as another input improved the performance in both training (ME 83.10 and 82.22%) and testing (ME 62.64 and 61.89%) marginally. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The Institution of Engineers (India): Series A Springer Journals

Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment

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References (9)

Publisher
Springer Journals
Copyright
Copyright © 2017 by The Institution of Engineers (India)
Subject
Engineering; Civil Engineering
ISSN
2250-2149
eISSN
2250-2157
DOI
10.1007/s40030-017-0203-5
Publisher site
See Article on Publisher Site

Abstract

In this study, an attempt has been made to develop an ANN model to estimate runoff from a snow covered catchment of eastern Himalaya using feed-forward back-propagation algorithm with Levenberg–Marquardt optimization technique. The ANN model was programmed in C++ whereas a user-friendly GUI was developed in VB. The effects of past days rainfall and present day temperature data was observed on the performance of the selected ANN architecture in modelling snowmelt and monsoon season runoff. For this purpose, 8 years’ (2003–2010) daily data (rainfall, temperature, and discharge) were collected from CWC which were again divided into two parts (2003–2008 and 2009–2010) for training and testing of the ANN model, respectively. Initially it was found that the network can produce acceptable results with only rainfall data as input, but it needs at least past 3 days rainfall data to account for the antecedent moisture condition of the catchment. Networks 4-16-16-1 (with past 3 days rainfall) and 6-18-18-18-1 (with past 5 days rainfall) resulted modelling efficiency of 79.38 and 82.06% in training and 55.13 and 61.06% in validation, respectively. However, addition of present day temperature data as another input improved the performance in both training (ME 83.10 and 82.22%) and testing (ME 62.64 and 61.89%) marginally.

Journal

Journal of The Institution of Engineers (India): Series A – Springer Journals

Published: Jun 14, 2017

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