Artificial Neural Networks in Hydrology. II: Hydrologic Applications

This paper forms the second part of the series on application of artificial neural networks (ANNs) in hydrology. The role of ANNs in various branches of hydrology has been examined here. It is found that ANNs are robust tools for modeling many of the nonlinear hydrologic processes such as rainfall-runoff, stream flow, ground-water management, water quality simulation, and precipitation. After appropriate training, they are able to generate satisfactory results for many prediction problems in hydrology. A good physical understanding of the hydrologic process being modeled can help in selecting the input vector and designing a more efficient network. However, artificial neural networks tend to be very data intensive, and there appears to be no established methodology for design and successful implementation. For this emerging technique to find application in engineering practice, there are still some questions about this technique that must be further studied, and important aspects such as physical interpretation of ANN architecture, optimal training data set, adaptive learning, and extrapolation must be explored further. The merits and limitations of ANN applications have been discussed, and potential research avenues have been explored briefly.