Flood frequency analysis at ungauged sites using artificial neural networks in canonical correlation analysis physiographic space

Abstract

Models based on canonical correlation analysis (CCA) and artificial neural networks (ANNs) are developed to obtain improved flood quantile estimates at ungauged sites. CCA is used to form a canonical physiographic space using the site characteristics from gauged sites. Then ANN models are applied to identify the functional relationships between flood quantiles and the physiographic variables in the CCA space. Two ANN models, the single ANN model and the ensemble ANN model, are developed. The proposed approaches are applied to 151 catchments in the province of Quebec, Canada. Two evaluation procedures, the jackknife validation procedure and the split sample validation procedure, are used to evaluate the performance of the proposed models. Results of the proposed models are compared with the original CCA model, the canonical kriging model, and the original ANN models. The results indicate that the CCA-based ANN models provide superior estimation than the original ANN models. The ANN ensemble approaches provide better generalization ability than the single ANN models. The CCA-based ensemble ANN model has the best performance among all models in terms of prediction accuracy. Copyright 2007 by the American Geophysical Union.