Evaluation of the Applicability of Some Methods to Estimate Instantaneous Peak Flow Using Daily Flow Data

This research was designed to evaluate applicability of some empirical methods including Fuller (1914)، Sangal (1983) and Fill and Steiner (2003) approaches as well as the ANN (Artificial Neural Networks) technique to prolong instantaneous peak flow data series using daily measured flow data. To do this، after initial consideration، a number of 12 gauging stations located in arid and semi-arid regions of the north half of Iran were selected and the related data was collected. Then، the applied empirical methods were calibrated for the related stations، and the instantaneous peak flow was estimated for some specific years. To be able to have a reliable comparison، the same years were also used in testing phase of the ANN modeling. Comparing the results shows higher ability of ANN over the empirical methods for this specific application، although there was a considerable improvement of the results for empirical methods after calibration. Reliable results produced by ANN method، compared to the traditional methods، in this research showed superior abilities of new machine learning techniques to solve the problem of inadequate measured peak flow data periods and to optimize water related designs.