Rainfall-runoff modelling using adaptive neuro-fuzzy systems

Rainfall-runoff modelling using adaptive neuro-fuzzy systems

Two important applications of rainfall-runoff models are forecasting and simulation. At present, rainfall-runoff models based on artificial intelligence methods are built basically for short-term forecasting purposes and these models are not very effective for simulation purposes. This study explores the applicability and effectiveness of adaptive neuro-fuzzy-system-based rainfall-runoff models...

Rainfall-Runoff Modelling Using Genetic Programming

The problem of accurately determining river flows from rainfall, evaporation and other factors, occupies an important place in hydrology. The rainfall-runoff process is believed to be highly non-linear, time varying, spatially distributed and not easily described by simple models. Practitioners in water resources have embraced data-driven modelling approaches enthusiastically, as they are perce...

Adaptive Neuro-Fuzzy Systems

One benefit of fuzzy systems (Zadeh, 1965; Ruspini et al., 1998; Cox, 1994) is that the rule base can be created from expert knowledge, used to specify fuzzy sets to partition all variables and a sufficient number of fuzzy rules to describe the input/output relation of the problem at hand. However, a fuzzy system that is constructed by expert knowledge alone will usually not perform as required...

Adaptive Neuro-Fuzzy Systems

Rainfall-runoff modelling using artificial neural networks (ANNs): modelling and understanding

In recent years, artificial neural networks (ANNs) have become one of the most promising tools in order to model complex hydrological processes such as the rainfall-runoff process. In many studies, ANNs have demonstrated superior results compared to alternative methods. ANNs are able to map underlying relationship between input and output data without prior understanding of the process under in...