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Erschienen in:

01.01.2016

Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform

verfasst von: Mohamed Shenify, Amir Seyed Danesh, Milan Gocić, Ros Surya Taher, Ainuddin Wahid Abdul Wahab, Abdullah Gani, Shahaboddin Shamshirband, Dalibor Petković

Erschienen in: Water Resources Management | Ausgabe 2/2016

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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 (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.

Vorheriger Artikel Incorporating Hydrologic Routing into Reservoir Operation Models: Implications for Hydropower Production Planning

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Titel: Precipitation Estimation Using Support Vector Machine with Discrete Wavelet Transform
verfasst von: Mohamed Shenify
Amir Seyed Danesh
Milan Gocić
Ros Surya Taher
Ainuddin Wahid Abdul Wahab
Abdullah Gani
Shahaboddin Shamshirband
Dalibor Petković
Publikationsdatum: 01.01.2016
Verlag: Springer Netherlands
Erschienen in: Water Resources Management / Ausgabe 2/2016
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI: https://doi.org/10.1007/s11269-015-1182-9

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