Regional flood frequency modeling: a comparative study among several data-driven models Original Paper First Online: 13 September 2019 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
Responsible Editor: Broder J. Merkel
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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