Estimation of water quality characteristics at ungauged sites using artificial neural networks and canonical correlation analysis

SummaryThree models are developed for the estimation of water quality mean values at ungauged sites. The first model is based on artificial neural networks (ANN), the second model is based on ensemble ANN (EANN) and the third model is based on canonical correlation analysis (CCA) and EANN. The ANN and EANN models are developed to establish the functional relationship between water quality mean values and basin attributes. In the CCA-based EANN model, CCA is used to form a canonical attributes space using data from gauged sites. Then, an EANN is applied to identify the functional relationships between water quality mean values and the attributes in the CCA space. Four water quality variables are selected as output of these models. Variable selection is based on principal component analysis. The water quality variables which showed the highest loading factors in the first four components are selected. The three models are applied to 50 subcatchments in the Nile Delta, Egypt. A jackknife validation procedure is used to evaluate the performance of the three models. The results show that the EANN model provides better generalization ability than the ANN. However, the CCA-based EANN model performed better than the other two models in terms of prediction accuracy.

► In this paper three models were developed for the estimation of water quality (WQ) characteristics at ungauged sites.
► The three models are based on simple ANN, ensemble ANN (EANN) and EANN in the canonical space (EANN-CCA) models.
► Models objective was to establish the functional relationship between WQ mean values and basin attributes.
► Results showed that the EANN model provides better generalization ability than the ANN.
► The EANN models in the CCA attributes space performed better than the ANN and EANN models in the original space.