Rainfall Runoff Modelling using Artificial Neural Network

The use of an artificial neural network (ANN) has become common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature and therefore, representing their physical characteristics is challenging. In this paper, the influences of back propagation algorithm and their efficiencies which affect the input dimensions on rainfall runoff model have been demonstrated. The capability of the Artificial Neural Network with different input dimensions have been attempted and demonstrated with a case study on Sarada River Basin. The ANN models developed were able to map relationship between input and output data sets used. The model developed on rainfall and runoff pattern have been calibrated and validated. The significant input variables for training of ANN models were selected based on statistical parameters like cross-correlation, autocorrelation, and partial autocorrelation function. Different combinations were used and six combinations were selected based on the statistics of these functions. It was found that those models considering rainfall lag rainfall and discharge as inputs were performing better than those considering rainfall alone. It was found that the neural network model developed was performing well. It can be inferred from the developed model that the Neural Network model was able to predict runoff from rain fall data fairly well for a small semi-arid catchment area considered in the present study.