Neural networks for prediction of stream flow based on snow accumulation

Sansiri Tarnpradab, Kishan Mehrotra, Chilukuri Mohan, David G. Chandler

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This study aims to improve stream-ow forecast at Reynolds Mountain East watersheds, which is located at the southernmost of all watersheds in Reynolds Creek Experimental Watershed Idaho, USA. Two separate models, one for the annual data and the other for the seasonal (April-June) data from 1983-1995 are tested for their predictability. Due to the difculties in collecting data during winter months, in particular the snow water equivalent (SWE), this study evaluates the impact of excluding this variable. Our results show that multilayer perceptrons (MLP) and support vector machines (SVM) are more suitable for modeling the data. The results also reveal that the difference between stream-ow forecast via annual and seasonal models is insignicant and for longer term predictions SWE is a strong driver in the stream-ow forecast. Principal Component Analysis (PCA) and Particle Swarm Optimization (PSO) are also used in this study to identify useful features. The results from PCA derived models show that PCA helps reduce prediction error and the results are more stable than using models without PCA. PSO also improved results; however, the set of selected attributes by PSO is less believable than given by PCA. The best prediction is achieved when MLP model is implemented with attributes generated by PCA.

Original languageEnglish (US)
Title of host publicationIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014
Subtitle of host publication2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages88-94
Number of pages7
ISBN (Electronic)9781479945108
DOIs
StatePublished - Jan 15 2014
Event2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014 - Orlando, United States
Duration: Dec 9 2014Dec 12 2014

Publication series

NameIEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings

Other

Other2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, CIES 2014
CountryUnited States
CityOrlando
Period12/9/1412/12/14

ASJC Scopus subject areas

  • Computer Science Applications
  • Artificial Intelligence
  • Computational Theory and Mathematics

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    Tarnpradab, S., Mehrotra, K., Mohan, C., & Chandler, D. G. (2014). Neural networks for prediction of stream flow based on snow accumulation. In IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings (pp. 88-94). [7011836] (IEEE SSCI 2014 - 2014 IEEE Symposium Series on Computational Intelligence - CIES 2014: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CIES.2014.7011836