S. S. Eslamian, S. Chavoshi Boroojeni,
Volume 7, Issue 1 (4-2003)
Abstract
Numerous methods are used in the investigation of floods in catchments such as regional flood frequency analysis. Regional flood frequency analysis relies on physical, climatic and ecological characteristics of catchments and applies statistical methods to study flow records. Hosking and Wallis developed Probability Weighted Moments and presented L-moments statistics as a new tool for flood frequency analysis. In this paper, the theory of L-moments was used to study the flood frequency of central catchments of Iran. A number of 27 sites each with more than 5 years of observed data were studied. In the first step, the diagram of L-kurtosis versus L-skewness was used and proper distributions for each site were applied. In order to eliminate the heterogeneous sites, homogeneous tests based on D, H1, H2 and H3 criteria were performed indicating that two sites appeared to be heterogenous. Next, using Goodness of Fit Test, the best regional distributions were determined which are GL, GEV, GN, PE3 and GPA, respectively. Finally, quantile estimates for distributions accepted at a 90% level were presented.
K. Ghaderi, B. Motamedvaziri, M. Vafakhah, A.a. Dehghani,
Volume 25, Issue 4 (3-2022)
Abstract
Proper flood discharge forecasting is significant for the design of hydraulic structures, reducing the risk of failure, and minimizing downstream environmental damage. The objective of this study was to investigate the application of machine learning methods in Regional Flood Frequency Analysis (RFFA). To achieve this goal, 18 physiographic, climatic, lithological, and land use parameters were considered for the upstream basins of the hydrometric stations located in Karkheh and Karun watersheds (46 stations with a statistical length of 21 years). The best Probability Distribution Function (pdf) was then determined using the Kolmogorov-Smirnov test at each station to estimate the flood discharge with a return period of 50-year using maximum likelihood methods and L-moments. Finally, RFFA was performed using a decision tree, Bayesian network, and artificial neural network. The results showed that the log Pearson type 3 distribution in the maximum likelihood method and the generalized normal distribution in the L moment method are the best possible regional pdfs. Based on the gamma test, the parameters of the perimeter, basin length, shape factor, and mainstream length were selected as the best input structure. The results of regional flood frequency analysis showed that the Bayesian model with the L moment method (R2 = 0.7) has the best estimate compared to other methods. Decision tree and artificial neural network were in the following ranks.
