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RAINFALL—RUNOFF MODELLING USING ARTIFICIAL NEURAL NETWORKS

Mallikarjuna, P. / Suresh Babu, C. H. / Reddy, A. Jagan Mohan

Runoff simulation and forecasting is essential for planning, designing and operation of water resources projects. In the present study, the rainfall—runoff process is modelled by coupling simple time—series (STS) and linear autoregressive (ARX) models with Artificial Neural Networks (ANN). The study uses the monthly data at Keesara, Damarcherla and Madhira gauging sites of Krishna basin. The study reveals that the performance of ANN model in the simulation and forecasting of monthly runoff during monsoon period can be improved considerably by including the residuals derived from STS and ARX models as additional inputs together with rainfall.

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RAINFALL—RUNOFF MODELLING USING ARTIFICIAL NEURAL NETWORKS

Mallikarjuna, P. / Suresh Babu, C. H. / Reddy, A. Jagan Mohan

Runoff simulation and forecasting is essential for planning, designing and operation of water resources projects. In the present study, the rainfall—runoff process is modelled by coupling simple time—series (STS) and linear autoregressive (ARX) models with Artificial Neural Networks (ANN). The study uses the monthly data at Keesara, Damarcherla and Madhira gauging sites of Krishna basin. The study reveals that the performance of ANN model in the simulation and forecasting of monthly runoff during monsoon period can be improved considerably by including the residuals derived from STS and ARX models as additional inputs together with rainfall.

RAINFALL—RUNOFF MODELLING USING ARTIFICIAL NEURAL NETWORKS

Mallikarjuna, P. / Suresh Babu, C. H. / Reddy, A. Jagan Mohan

Runoff simulation and forecasting is essential for planning, designing and operation of water resources projects. In the present study, the rainfall—runoff process is modelled by coupling simple time—series (STS) and linear autoregressive (ARX) models with Artificial Neural Networks (ANN). The study uses the monthly data at Keesara, Damarcherla and Madhira gauging sites of Krishna basin. The study reveals that the performance of ANN model in the simulation and forecasting of monthly runoff during monsoon period can be improved considerably by including the residuals derived from STS and ARX models as additional inputs together with rainfall.

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Title:

RAINFALL—RUNOFF MODELLING USING ARTIFICIAL NEURAL NETWORKS

Contributors:

Mallikarjuna, P. (author) / Suresh Babu, C. H. (author) / Reddy, A. Jagan Mohan (author)

Published in:

ISH Journal of Hydraulic Engineering ; 15 ; 24-33

Publication date:

2009-01-01

Size:

10 pages

ISSN:

0971-5010 , 2164-3040

DOI:

https://doi.org/10.1080/09715010.2009.10514928

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

Simple time-series model , Linear autoregressive model , Artificial neural network , Combination , Performance evaluation

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