Hydrologic Applications of MRAN Algorithm

Abstract

Applications of artificial neural networks in simulation and forecasting of hydrologic systems have a long record and generally promising results. Most of the earlier applications were based on the back-propagation (BP) feed-forward method, which used a trial-and-error to determine the final network parameters. The minimal resource allocation network (MRAN) is an on-line adaptive method that automatically configures the number of hidden nodes based on the input–output patterns presented to the network. Numerous MRAN applications in various fields such as system identification and signal processing demonstrated flexibility of the MRAN approach and higher or similar accuracy with more compact networks, compared to other learning algorithms. This research introduces MRAN and assesses its performance in hydrologic applications. The technique was applied to an agricultural watershed in central Illinois to predict daily runoff and nitrate–nitrogen concentration, and the predictions were more accurate compared to the BP model.