Capability Evaluation of Hybrid Wavelet-Principal Component Analysis-Random Forest Approach in Simulating the River Flow

Document Type : Research Article

Authors

1 Water engineering department, Faculty of civil and surveying engineering, Graduate University of Advanced technology, Kerman, Iran

2 Water Engineering Department, Civil and surveying engineering Faculty, Graduate University of Advanced Technology

Abstract

Simulating the flow for managing the water allocation in drought and wet periods is of great importance. According to the researches conducted during several decades in this regard, computational intelligence methods combined with wavelets are known to be effective. In this paper, Wavelet-Principal Component Analysis-Random Forest (WPCARF) hybrid approach is proposed to model the daily flow of the Polroud river. In the proposed model, first, hydrometric data is preprocessed by wavelet transform and applied to the PCA along with meteorological data. Afterward, their output vectors were entered into the random forest network. The results have shown that the PCA algorithm can improve the performance accuracy and speed of the model, despite reducing the input vectors and simplifying them. Also, it can integrate a model with increased simulation time and input vectors uncertainty having a lower impact on model capability leading to a more uniform decreasing trend. Furthermore, preprocessing the data accompanied by PCA could enhance the agreement index by 5 and 8 percent during one and three days of the simulation and increase the model ability for a more accurate simulation of river flow. On the other hand, results for the best-proposed hybrid model during the one-day-ahead simulation time were R=0.911 and RMSE=7.095 m3/s, while these values were R=0.817 and RMSE=8.681 m3/s in the best hybrid model for three-day-ahead simulation time. This indicates the adequate capacity of the proposed hybrid model for long-term simulation times.

Keywords

Main Subjects

References

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Amirkabir Journal of Civil Engineering
Volume 53, Issue 7
October 2021
Pages 2837-2850

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