The estimation of flood quantiles in ungauged sites using Teaching-Learning Based Optimization and Artificial Bee Colony algorithms

Abstract

In this study a Regional Flood Frequency Analysis (RFFA) was applied to 33 stream gauging stations in the Eastern Black Sea Basin, Turkey Homogeneity of the region was determined by discordancy (Di) and heterogeneity measures (Hi) based on L-moments. Generalized extreme-value, lognormal, Pearson type III, and generalized logistic distributions were fitted to the flood data of the homogeneous region. Based on the appropriate distribution for the region, flood quantiles were estimated for return periods of T = 5,10, 25, 50,100, and 500 years. A non-linear regression model was then developed to determine the relationship between flood discharges and meteorological and hydrological characteristics of the catchment. In order to compare regression analysis with other models, Artificial Bee Colony algorithm (ABC) and Teaching-Learning Based Optimization (TLBO) models were developed. The equations were obtained using the ABC and TLBO algorithms to estimate flood discharges for different return periods. The analysis showed that the TLBO and ABC results were superior to the regression analysis. Error values indicated that the TLBO method yielded better results for the estimation of flood quantiles for different independent variables.