首页|Data mining based on wavelet and decision tree for rainfall-runoff simulation

Data mining based on wavelet and decision tree for rainfall-runoff simulation

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  • NETL
  • NSTL
  • Iwa Publishing
This study introduced a new hybrid model (Wavelet-M5 model) which combines the wavelet transforms and M5 model tree for rainfall-runoff modeling. For this purpose, the main time series were decomposed to several sub-signals by the wavelet transform, at first. Then, the obtained sub-time series were imposed as input data to M5 model tree, and finally, the related linear regressions were presented by M5 model tree. This new technique was applied on the monthly time series of Sardrud catchment and the results were also compared with other models like WANN and sole M5 model tree. The results showed that the accuracy of the proposed model is better than the previous models and also indicated the effect of data pre-processing on the performance of M5 model tree. The determination coefficient of the training stage was 0.80 and improved 31% than the M5 model tree for Sardrud catchment which is recognized as a normal watershed with a regular four seasons' pattern.

decision treeM5 model treemulti-linear modelrainfall-runoff modelingwavelet transform

Nourani, Vahid、Tajbakhsh, Ali Davanlou、Molajou, Amir

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Univ Tabriz, Fac Civil Engn, Dept Water Resources Engn, POB 51666, Tabriz, Iran|Near East Univ, Dept Civil Engn, POB 99138,Mersin 10, Nicosia, North Cyprus, Turkey

Univ Tabriz, Fac Civil Engn, Dept Water Resources Engn, POB 51666, Tabriz, Iran

Iran Univ Sci & Technol, Fac Civil Engn, Dept Water Resources Engn, Tehran, Iran

2019

10.2166/nh.2018.049

Hydrology research: An international journal

Hydrology research: An international journal

EIISTP
ISSN：1672-7118
年,卷(期)：2019.50(1/2)
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