Rainfall Runoff Analysis using Artificial Neural Network

Background/Objective: The main objective of the present study is to conduct laboratory experiment for the generation of rainfall runoff data using rainfall simulator. For the validation this observed data, a model is establish for estimating observed runoff data using Artificial Neural Network (ANN) technique. Methods: A total 12 laboratory experiments were conducted using rainfall simulator to generate runoff hydrograph using various slope and rainfall intensity over the catchment. For the validation of observed runoff hydrograph data were simulate using ANN. The ANN model was developed using collected 1076 data point to compute runoff discharge. For developing ANN model, the available data were separated as 70% for training, 15% for testing and 15% for validation. Results: The predicted results using ANN model performed better estimation with observed values which is useful for water resources planning and management etc. For the testing of model performance Nash-Sutcliffe efficiency criteria were used which gives NSE greater than 95%. Conclusion: The comparison of observed and predicted runoff hydrograph reveals that the Artificial Neural Network (ANN) predicts the runoff data reasonably well in observed hydrograph. It is found that ANNs are promising tools not only in accurate modeling of complex processes but also in providing insight from the learned relationship, which would assist the modeler in understanding of the process under investigation as well as in evaluation of the model.