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Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform
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DOI：10.1007/s11269-015-1182-9.png)
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Abstract
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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.
Keywords:
Precipitation
Support vector machine
Discrete wavelet transform
Genetic programming
Artificial neural network
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