LAUSR

Abstract Precise and timely predictions of high and low flow events at any watershed location can provide stakeholders the information required to make strategic, informed decisions. Specifically, soft computing models have become popular in last few decades because of their ability of offering effective solutions for modeling and analyzing the behavior of complex dynamical systems along with simulating and forecasting hydrological applications. Artificial neural networks (ANN) have gained significant attention for forecasting stream flows in last two decades. Phase lag nevertheless is the common problem noticed in most of the studies that used univariate time series modeling. This technical issue must be resolved if such tools are to be transferred into an operational settings. This study explores the potential benefits of applying a phase lag correction procedure to neural network stream flow forecasting models by combining wavelet transform with ANN at four stations from Krishna and Narmada basins in India.