Modified NLPM-ANN model and its application

Abstract

Summary The non-linear perturbation model based on artificial neural network (NLPM-ANN) takes advantage of the consideration of seasonal information by the linear perturbation model (LPM) and the notable non-linear simulation capability of artificial neural network (ANN). However, this model does not take account of antecedent catchment wetness that may effect the simulation and forecasting accuracy. A modified NLPM-ANN model is proposed and developed to take the consideration of antecedent catchment wetness. The output perturbing terms of the response function in the simple linear model (SLM) in an auxiliary component are taken as inputs of ANN to represent catchment wetness. The simulated total runoff is obtained by integrating the outputs of ANN with that of the seasonal model. The rainfall–runoff data of eight catchments were selected and used to compare the modified NLPM-ANN with the NLPM-ANN models. Results show that the modified NLPM-ANN is significantly superior to the NLPM-ANN, and the model component efficiency index values are 16.82% and 16.74% over the NLPM-ANN during calibration and verification periods, respectively.