Flood Frequency Analysis - A Comparative Study of ANN and ANFIS

Abstract

The frequency of occurrence of extreme hydrologic event like flood is important in water resources planning and management. Though there is a definite relationship between the frequency of occurrences and magnitude of the extreme event, reliable prediction of maximum discharge remains as a challenge due to uncertainties and non-linearity of influencing parameters. Statistical techniques are commonly used for finding the maximum discharge and return period relationship. However, these techniques are generally considered to be inadequate because of the non-linearity of the problem. In this study, artificial neural network and adaptive neuro-fuzzy inference system are employed in order to capture the non-linear relationship between annual maximum discharge and frequency. The developed models are validated using Godavari River basin data. Performances of the developed models were compared with respect to root mean square errors, efficiency and coefficient of determination. Based on these results, it was found that artificial neural network performs marginally better than that of adaptive neuro-fuzzy inference system.