Validation of an ANN Flow Prediction Model Using a Multistation Cluster Analysis

Abstract

The objective of this study is to validate a flow prediction model for a hydrometric station using a multistation criterion in addition to standard single-station performance criteria. In this contribution we used cluster analysis to identify the regional flow height, i.e., water-level patterns and validate the output of an artificial neural network (ANN) model of the Alportel River in Portugal. Measurements of precipitation, temperature, and flow height were used as input variables to the ANN model with a lead time of 12 h. The lead time of 12 h is assumed to be appropriate for a short-term hydrological prediction since it is meaningful for physical processes. The ANN model with three inputs, four hidden neurons, and ten epochs was tested using the new model-validation criterion. The high performance of the model (i.e., Nash-Sutcliffe coefficient is equal to 0.922) was confirmed by the cluster-analysis criterion. It can be concluded that a multistation-based approach can be used as an additional validation criterion and might result in a rejection of a model which initially passed a single-station validation criterion.