Regional Flood Analysis Via Comparison of The M5 Decision Tree Algorithm and Regression Models

Document Type : Research Paper


1 MSc of Hydrology and Water resource Engineering Department of Shahid Chamran University, Ahvaz, Iran.

2 professor, faculty member of hydrology and water resource engineering Department of Shahid Chamran University, Ahvaz, Iran

3 Assistant professor, faculty member of hydrology and water resource engineering Department of Shahid Chamran University, Ahvaz, Iran

4 Assistant professor, faculty member of Agriculture and Natural Resources University, Ahvaz, Iran


Developing of techniques for regional flood frequency estimation in ungauged sites is one of the foremost goals of contemporary hydrology. The flood frequency evaluation for ungauged catchments is usually approached by deriving suitable statistical relationships (models) between flood statistics and basins characteristics. Already, several equations have been presented to estimate the flood frequency in different areas such as Karkheh basin. However, due to the complexity of this phenomenon, the relationships have not been capable to simulate the flood frequency with desired accuracy. Accordingly, in this study, in addition to the regression method has been used in the previous studies, the ANN and ANFIS models are applied.  In fact, these are a type of black box models without any knowledge of processes within the system, in which inputs are converted into outputs (or output). This situation indicates that this type of new models is actually similar to the regression relations, however, there is further flexibility in adjusting the weights and thus can be used as a replacement to multivariate regressions.


Main Subjects

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Volume 40, Issue 4
February 2018
Pages 183-195
  • Receive Date: 15 July 2016
  • Revise Date: 08 October 2016
  • Accept Date: 19 October 2016
  • Publish Date: 21 January 2018