Estimation of Instantaneous Peak Flow Using Machine-Learning Models and Empirical Formula in Peninsular Spain

The design of hydraulic structures and flood risk management is often based on instantaneous peak flow (IPF). However, available flow time series with high temporal resolution are scarce and of limited length. A correct estimation of the IPF is crucial to reducing the consequences derived from flash floods, especially in Mediterranean countries. In this study, empirical methods to estimate the IPF based on maximum mean daily flow (MMDF), artificial neural networks (ANN), and adaptive neuro-fuzzy inference system (ANFIS) have been compared. These methods have been applied in 14 different streamflow gauge stations covering the diversity of flashiness conditions found in Peninsular Spain. Root-mean-square error (RMSE), and coefficient of determination (R-2) have been used as evaluation criteria. The results show that: (1) the Fuller equation and its regionalization is more accurate and has lower error compared with other empirical methods; and (2) ANFIS has demonstrated a superior ability to estimate IPF compared to any empirical formula.