alexa Investigation Of Sensitivity Of Popular Training Methods To Initial Weights In ANN Rainfall-runoff Modeling
ISSN: 2157-7587

Hydrology: Current Research
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Share This Page

Additional Info

Loading
Loading Please wait..
 

3rd International Conference on Hydrology & Meteorology
September 15-16, 2014 Hyderabad International Convention Centre, India

Vikas Kumar Vidyarthi and Ashu Jain
Young Research Forum: Hydrol Current Res
DOI: 10.4172/2157-7587.S1.013
Abstract
Runoff estimation is a key input in any water resource management activity. It is generally estimated by developing rainfallrunoff (RR) models. There are many techniques employed for RR modeling and artificial neural network (ANN) is one of the popular methods among them. The gradient descent (GD) and Levenberg-Marquardt (LM) optimization methods are commonly adopted algorithms for the training of ANN models. It has been reported that the performance of these algorithms is always sensitive to their initial weights. In this paper, the sensitivity of these two training algorithms to initial weights in the performance of ANN-RR model was investigated. The best ANN architecture was determined using a trial and error procedure in which the number of hidden neurons was varied from 1 to 20 and the architecture giving best performance in terms of certain error statistics was selected as the best. Each of the twenty architectures was trained using BPA and LMA and the best architecture was selected, named ANN-BPA and ANN-LMA, respectively. Then, these best ANN architectures were trained on ten different set of initial weights using both BPA and LMA. The performance of the best ANN model trained by BPA and LMA on different initial weights was then compared using standard error statistical measures. The daily rainfall, runoff data derived from Bird creek basin, Oklahoma, USA have been employed to develop all the models included here. The input variables were selected on the basis of correlation analysis. The performance evaluation statistics such as average absolute relative error (AARE), Pearson?s correlation coefficient (R) and threshold statistics (TS) were used for comparing all the models developed using both the optimization algorithm here. Based on the results obtained in this study, it has been found that the LMA trained ANN model performed better than the BPA trained ANN model. Further, the LMA trained ANN model is found to be more robust than the BPA trained ANN model as the ten different set of initial weights result into final solution similar to each other in case of the LMA trained ANN models.
Biography
Vikas Kumar Vidyarthi is a PhD student in the Department of Civil Engineering, IIT Kanpur.
image PDF   |   image HTML
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri, Food, Aqua and Veterinary Science Journals

Dr. Krish

[email protected]

1-702-714-7001 Extn: 9040

Clinical and Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemical Engineering and Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001 Extn: 9040

Earth & Environmental Sciences

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

General Science and Health care Journals

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics and Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001 Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Informatics Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Material Sciences Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Mathematics and Physics Journals

Jim Willison

[email protected]

1-702-714-7001 Extn: 9042

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001 Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

John Behannon

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001 Extn: 9042

 
© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords