Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates


JOURNAL OF HYDROLOGY, vol.375, pp.481-488, 2009 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 375
  • Publication Date: 2009
  • Doi Number: 10.1016/j.jhydrol.2009.06.051
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.481-488
  • Keywords: River flow estimation, Artificial neural network, Fuzzy c-means clustering
  • Middle East Technical University Affiliated: Yes


Reliable river flow estimates are crucial for appropriate water resources planning and management. River flow forecasting can be conducted by conceptual or physical models, or data-driven black box models. Development of physically-based models requires an understanding of ail the physical processes which impact a natural process and the interactions among them. Since identification of the relationships among these physical processes is very difficult, data-driven approaches have recently been utilized in hydrological modeling. Artificial neural networks are one of the widely used data-driven approaches for modeling hydrological processes. In this study, estimation of future monthly river flows for Guvenc River, Ankara is conducted using various artificial neural network models. Success of artificial neural network models relies on the availability of adequate data sets. A direct mapping from inputs to outputs without consideration of the complex relationships among the dependent and independent variables of the hydrological process is identified. In this study, past precipitation, river flow data, and the associated month are used to predict future river flows for Guvenc River. Impacts of various input patterns, number of training cycles, and initial values assigned to the weights of the connections are investigated. One of the major weaknesses of artificial neural networks is that they may fail to generate good estimates for extreme events, i.e. events that do not occur at all or often enough in the training data set. It is very important to be able to identify such unlikely events. A fuzzy c-means algorithm is used in this study to cluster the training and validation input vectors into regular and extreme events so that the user will have an idea about the risk of the artificial neural network model to generate unreliable results. (C) 2009 Elsevier B.V. All rights reserved.