Fuzzy computing based rainfall-runoff model for real time flood forecasting

Abstract

Abstract:This paper analyses the skills of fuzzy computing based rainfall-runoff model in real time flood forecasting. The potential of fuzzy computing has been demonstrated by developing a model for forecasting the river flow of Narmada basin in India. This work has demonstrated that fuzzy models can take advantage of their capability to simulate the unknown relationships between a set of relevant hydrological data such as rainfall and river flow. Many combinations of input variables were presented to the model with varying structures as a sensitivity study to verify the conclusions about the coherence between precipitation, upstream runoff and total watershed runoff. The most appropriate set of input variables was determined, and the study suggests that the river flow of Narmada behaves more like an autoregressive process. As the precipitation is weighted only a little by the model, the last time-steps of measured runoff are dominating the forecast. Thus a forecast based on expected rainfall becomes very inaccurate. Although good results for one-step-ahead forecasts are received, the accuracy deteriorates as the lead time increases. Using the one-step-ahead forecast model recursively to predict flows at higher lead time, however, produces better results as opposed to different independent fuzzy models to forecast flows at various lead times.

The variation of discharge entering the Niger Delta system, 1951–2000, and estimates of change under global warming

“…Most techniques, employed in this regard, have involved rainfall-runoff relationships that are in most cases based on linearity or piecewise linearity (Hsu et al, 1995). Others used rule-based systems that are governed by fuzzy inference rules (Nayak et al, 2005). This study makes a difference by testing a case sensitive approach of the variables (rainfall-discharge) in the basin, exploring various fits and suggesting fits best suited for rainfall-discharge correlation.…”

Multi-objective optimization of typhoon inundation forecast models with cross-site structures for a water-level gauging network by integrating ARMAX with a genetic algorithm

“…Chang and Chen (2001) proposed a counter-propagation fuzzy-neural network capable of automatically generating rules for use in clustering input data to enable streamflow prediction. Nayak et al (2005) employed fuzzy computation in the development of a realtime flood forecasting model. They concluded that the recursive use of a one-step-ahead forecast model to predict flow using longer lead times produces results better than those achieved using independent fuzzy models for the forecasting of flow under various lead times.…”

Daily Runoff Forecasting Model Based on ANN and Data Preprocessing Techniques

“…On the contrary, black-box modeling does not require a deep knowledge of the underlying physics and also can solve the problem of the scarcity of the data. Several black-box models have been developed and used in hydrological forecasting, such as fuzzy theory [2,3], artificial neural network [4,5], chaos [6], genetic programming [7], support vector machine [8], and so on.…”