Comparison of Conceptual and Neural Network Rainfall-Runoff Models

Thursday, 18 December 2014
Vikas Kumar Vidyarthi and Ashu Jain, Indian Institute of Technology Kanpur, Department of Civil Engineering, Kanpur, India
Rainfall-runoff (RR) model is a key component of any water resource application. There are two types of techniques usually employed for RR modeling: physics based and data-driven techniques. Although the physics based models have been used for operational purposes for a very long time, they provide only reasonable accuracy in modeling and forecasting. On the other hand, the Artificial Neural Networks (ANNs) have been reported to provide superior modeling performance; however, they have not been acceptable by practitioners, decision makers and water resources engineers as operational tools. The ANNs one of the data driven techniques, became popular for efficient modeling of the complex natural systems in the last couple of decades. In this paper, the comparative results for conceptual and ANN models in RR modeling are presented. The conceptual models were developed by the use of rainfall-runoff library (RRL) and genetic algorithm (GA) was used for calibration of these models. Feed-forward neural network model structure trained by Levenberg-Marquardt (LM) training algorithm has been adopted here to develop all the ANN models. The daily rainfall, runoff and various climatic data derived from Bird creek basin, Oklahoma, USA were employed to develop all the models included here. Daily potential evapotranspiration (PET), which was used in conceptual model development, was calculated by the use of Penman equation. The input variables were selected on the basis of correlation analysis. The performance evaluation statistics such as average absolute relative error (AARE), Pearson’s correlation coefficient (R) and threshold statistics (TS) were used for assessing the performance of all the models developed here. The results obtained in this study show that the ANN models outperform the conventional conceptual models due to their ability to learn the non-linearity and complexity inherent in data of rainfall-runoff process in a more efficient manner. There is a strong need to carry out such studies to prove the superiority of ANN models over conventional methods in an attempt to make them acceptable by water resources community responsible for the operation of water resources systems.