Research Article
A WAVELET TRANSFORMATION-GENETIC ALGORITHM-ARTIFICIAL NEURAL NETWORK COMBINED MODEL FOR PRECIPITATION FORECASTING
Abstract
Black box models are one of the most common hydrological
models in order to make predictions of hydrological variables such as
precipitation and stream flow. In this study, performance of a combined model
which consists of wavelet transformation, genetic algorithm and artificial
neural network (WGANN) were tested for
prediction of monthly precipitation by using North Atlantic Oscillation (NAO)
index, Southern Oscillation (SO) index and precipitation data as input in the
model. The case study was carried out for Antalya which is located in Mediterranean
region of Turkey. As a result, it was attained that WGANN model performed more
successful than usual artificial neural network (ANN), multiple linear
regression (MLR) and genetic algorithm-artificial neural network (GANN) models.
Keywords
Artificial neural network genetic algorithm precipitation regression wavelet transformation
References
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Year 2017,
Issue: 1, 372 - 378, 09.11.2017
Abstract
References
- Alp, M., & Cigizoglu, H. K. (2007). Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environmental Modelling & Software, 22(1), 2-13. Chen, Y. H., & Chang, F. J. (2009). Evolutionary artificial neural networks for hydrological systems forecasting. Journal of Hydrology, 367(1), 125-137. Daliakopoulos, I. N., Coulibaly, P., & Tsanis, I. K. (2005). Groundwater level forecasting using artificial neural networks. Journal of Hydrology, 309(1), 229-240. Gao, C., Gemmer, M., Zeng, X., Liu, B., Su, B., & Wen, Y. (2010). Projected streamflow in the Huaihe River Basin (2010–2100) using artificial neural network. Stochastic Environmental Research and Risk Assessment, 24(5), 685-697. Hamed, M. M., Khalafallah, M. G., & Hassanien, E. A. (2004). Prediction of wastewater treatment plant performance using artificial neural networks. Environmental Modelling & Software, 19(10), 919-928. Kim, T. W., & Valdés, J. B. (2003). Nonlinear model for drought forecasting based on a conjunction of wavelet transforms and neural networks. Journal of Hydrologic Engineering, 8(6), 319-328. Mallat, S. G. (1989). A theory for multiresolution signal decomposition: the wavelet representation. IEEE transactions on pattern analysis and machine intelligence, 11(7), 674-693. Meyer, Y., (1993). Wavelets algorithms & applications. Society for Industrial and Applied Mathematics, Philadelphia. Nasseri, M., Asghari, K., & Abedini, M. J. (2008). Optimized scenario for rainfall forecasting using genetic algorithm coupled with artificial neural network. Expert Systems with Applications, 35(3), 1415-1421. North Atlantic Oscillation (NAO) Index, Climate Prediction Center, National Weather Service, NOAA, (online). Retrieved from (10.06.2017) URL:http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii.table Partal, T., & Cigizoglu, H. K. (2008). Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. Journal of hydrology, 358(3), 317-331. Partal, T., & Cigizoglu, H. K. (2009). Prediction of daily precipitation using wavelet—neural networks. Hydrological sciences journal, 54(2), 234-246. Partal, T. (2017). Wavelet regression and wavelet neural network models for forecasting monthly streamflow. Journal of Water and Climate Change, 8(1), 48-61. Ramirez, M. C. V., de Campos Velho, H. F., & Ferreira, N. J. (2005). Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. Journal of hydrology, 301(1), 146-162. Sahay, R. R., & Srivastava, A. (2014). Predicting monsoon floods in rivers embedding wavelet transform, genetic algorithm and neural network. Water resources management, 28(2), 301-317. Sedki, A., Ouazar, D., & El Mazoudi, E. (2009). Evolving neural network using real coded genetic algorithm for daily rainfall–runoff forecasting. Expert Systems with Applications, 36(3), 4523-4527. Shoaib, M., Shamseldin, A. Y., & Melville, B. W. (2014). Comparative study of different wavelet based neural network models for rainfall–runoff modeling. Journal of hydrology, 515, 47-58. Southern Oscillation (SO) Index, Climate Prediction Center, National Weather Service, NOAA, (online). Retrieved from (12.06.2017) URL: http://www.cpc.ncep.noaa.gov/data/indices/soi Zorn, C. R., & Shamseldin, A. Y. (2015). Peak flood estimation using gene expression programming. Journal of Hydrology, 531, 1122-1128.
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Details
| Subjects | Engineering |
|---|---|
| Journal Section | Articles |
| Authors | |
| Publication Date | November 9, 2017 |
| Published in Issue | Year 2017Issue: 1 |
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