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Streamflow and Sediment Load Prediction Using Linear Genetic Programming
- Source :
- Uludağ University Journal of The Faculty of Engineering, Vol 23, Iss 2, Pp 323-332 (2018)
- Publication Year :
- 2018
- Publisher :
- Bursa Uludag University, 2018.
-
Abstract
- Daily flow and suspended sediment discharge are two major hydrologıcal variables that affect rivers’ morphology and ecosystem, particularly during flood events. Artificial neural networks (ANNs) have been successfully used to model and predict these variables in recent studies. However, these are implicit and cannot be simply used in practice. In this paper, linear genetic programming (LGP) approach has been suggested to develop explicit models to predict these variables in two rivers in Iran. The explicit relationships (prediction rules) evolved by LGP take the form of equations or program codes, which can be checked for its physical consistency. The results showed that the LGP outperforms ANNs in terms of root mean squared error and coefficient of efficiency.
Details
- Language :
- English, Turkish
- ISSN :
- 21484147 and 21484155
- Volume :
- 23
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- Uludağ University Journal of The Faculty of Engineering
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.20e578a183c445ebb5f88ce143f92f03
- Document Type :
- article
- Full Text :
- https://doi.org/10.17482/uumfd.352833