Precipitation estimation using support vector machine with discrete wavelet transform

• Published in: Water Resources Management
• Main Author: Shenify M.; Danesh A.S.; Gocić M.; Taher R.S.; Wahab A.W.A.; Gani A.; Shamshirband S.; Petković D.
• Format: Article
• Language: English
• Published: Springer Netherlands 2015
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84982818428&doi=10.1007%2fs11269-015-1182-9&partnerID=40&md5=8a14782465bf0af5c4f06fb6bff82c05
• ISSN: 9204741
• DOI: 10.1007/s11269-015-1182-9

Precipitation prediction is of dispensable importance in many hydrological applications. In this study, monthly precipitation data sets from Serbia for the period 1946-2012 were used to estimate precipitation. To fulfil this objective, three mathematical techniques named artificial neural network (ANN), genetic programming (GP) and support vector machine with wavelet transform algorithm (WT-SVM) were applied. The mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), Pearson correlation coefficient (r) and coefficient of determination (R2) were used to evaluate the performance of the WT-SVM, GP and ANN models. The achieved results demonstrate that the WT-SVM outperforms the GP and ANN models for estimating monthly precipitation. © Springer Science+Business Media Dordrecht 2015.