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Arabian Journal of Geosciences

, 12:588 | Cite as

Regional flood frequency modeling: a comparative study among several data-driven models

  • Kamal Ghaderi
  • Baharak Motamedvaziri
  • Mehdi VafakhahEmail author
  • Amir Ahmad Dehghani
Original Paper
  • 23 Downloads

Abstract

Correct prediction of flood discharge is important to design the hydraulic structures, to diminish the danger of its failure, and to minimize the environmental damages of downstream. The present study aims to investigate the application of machine learning methods for regional flood frequency analysis (RFFA). To achieve this aim, a number of 18 parameters of physiographic, climatic, lithology, and land use for the upstream watershed of stations were considered. Then, the best regional probability distribution function (PDF) was determined through the Kolmogorov-Smirnov (KS) test in each station for estimation of flood discharge with a return period of 50 years (Q50). The best input combination and best length of training datasets were determined by Gamma and M tests, respectively. Finally, RFFA was performed using adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM), and genetic expression programming (GEP). Log-Pearson type III was identified as the best regional probability distribution based on the KS test for estimation of Q50. Gamma test results indicated that parameter of the perimeter, basin length, form factor, and mainstream length were selected as best input combination for RFFA. Also, according to the M-test results, the best length of training and testing datasets, 68% and 32% respectively, was determined. RFFA results indicated that the SVM, ANFIS, and GEP models had “good” performance (Nash-Sutcliff coefficient, NSE, equal to 0.75, 0.74, and 0.75, respectively).

Keywords

Regionalization Modeling Flood damage Input selection Machine learning models 

Notes

Acknowledgments

The authors would like to thank the Iran Water Resources Management Company (IWRMC), the Iran National Cartographic Center (INCC), the Geological Survey of Iran (GSI), and the Forests, Range and Watershed Management Organization (FRWMO) of Iran for providing the hydro-climatic data, the digital elevation model, the lithology map, and land uses of the study area, respectively.

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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Forest, Range and Watershed Management, Faculty of Natural Resources and Environment, Science and Research BranchIslamic Azad UniversityTehranIran
  2. 2.Department of Watershed Management Engineering, Faculty of Natural ResourcesTarbiat Modares UniversityNoorIran
  3. 3.Department of Water EngineeringGorgan University of Agricultural Sciences and Natural ResourcesGorganIran

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