Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

Mohamed Shenify, Amir Danesh, Milan Gocić, Ros Taher, Ainuddin Abdul Wahab, Abdullah Gani, Shahaboddin Shamshirband () and Dalibor Petković

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2016, vol. 30, issue 2, 652 pages

Abstract: 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 (R 2 ) 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. Copyright Springer Science+Business Media Dordrecht 2016

Keywords: Precipitation; Support vector machine; Discrete wavelet transform; Genetic programming; Artificial neural network (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s11269-015-1182-9

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