期刊
WATER RESOURCES MANAGEMENT
卷 30, 期 2, 页码 641-652出版社
SPRINGER
DOI: 10.1007/s11269-015-1182-9
关键词
Precipitation; Support vector machine; Discrete wavelet transform; Genetic programming; Artificial neural network
资金
- Malaysian Ministry of Higher Education under the University of Malaya High Impact Research Grant [UM.C/625/1/HIR/MoHE/FCSIT/17]
- Ministry of Education, Science and Technological Development, Republic of Serbia [TR37003]
- ICT COST Action Computationally-intensive methods for the robust analysis of non-standard data (CRoNoS) [IC1408]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据