Prediction of watershed runoff using Bayesian concepts and modular neural networks

Abstract

Artificial neural networks have been widely used as models for a variety of nonlinear hydrologic processes including that of predicting runoff over a watershed. In this paper such networks were organized in a modular architecture to handle complex sets of rainfall‐runoff data. Such data often contain examples corresponding to different rules that may be associated with high, low, and medium streamflows. Different modules within the network were trained to learn subsets of the input space in an expert fashion. A gating network was used to mediate the response of all the experts. The problem was posed as one of Bayesian statistics combined with maximum likelihood estimation of network parameters. The training of the gating network was equivalent to the classification problem (i.e., identification of the expert), while the experts trained to minimize the absolute difference between predicted and target monthly discharges. The performance of modular networks in predicting runoff over three medium‐sized watersheds was examined. Average monthly rainfall of current and previous months and average monthly temperatures were treated as network inputs, and monthly runoff was treated as output. Three different modular network architectures were examined in this paper: two based on hard classification and one based on soft classification. In addition, a fully connected feed forward network was utilized for comparison purposes. On the basis of the results, modular networks appear to be good alternatives for predicting runoff. The role of these networks on issues regarding probabilistic interpretability and classification are discussed.