Evaluation of regional flood quantiles at ungauged sites by employing nonlinearity-based clustering approaches.

Abstract

Water resource engineers extensively use regional flood frequency analysis to compute the discharge at ungauged sites with limited flow records in the river basins. Frequency and magnitude are the two important factors required to be analyzed for an effective assessment of flood disaster risk management. Globally, many linear clustering techniques are employed to categorize the watershed which are ineffective when dealing with noise and outliers. The present study overcomes this by proposing a relatively new nonlinear clustering algorithm based on hierarchical estimation of densities (NLCAHD) for the Cauvery basin, where the Homogeneity test (H) is enforced to identify the group of stations with same populations. Discordancy measure is carried out for screening the data in order to eliminate the conflicting sites from the group. The whole basin is classified into six homogeneous clusters, while the goodness of fit measure tests the data to distinguish the preferred distribution for the purpose of calculating the growth curves. A comparative study is made with the other linear algorithms such as K-means and C-means, which reveals the better performance of the proposed nonlinear model for identifying the homogeneous regions, in arriving at precise estimates of flood quantiles for various return periods up to 160 years.