Fuzzy conceptual rainfall-runoff models

A fuzzy conceptual rainfall-runoff (CRR) framework is proposed herein to de al with those parameter uncertainties of conceptual rainfall-runoff models, that are related to data and/or model structure: with every element of the rainfall-runoff model assumed to be possibly uncertain, taken here as bein g fuzzy. First, the conceptual rainfall-runoff system is fuzzified and then different operational modes are formulated using fuzzy rules; second, the parameter identification aspect is examined using fuzzy regression techniqu es. In particular, bi-objective and tri-objective fuzzy regression models a re applied in the case of linear conceptual rainfall-runoff models so that the decision maker may be able to trade off prediction vagueness (uncertain ty) and the embedding outliers. For the non-linear models, a fuzzy least sq uares regression framework is applied to derive the model parameters. The m ethodology is illustrated using: (1) a linear conceptual rainfall-runoff mo del; (2) an experimental two-parameter model; and (3) a simplified version of the Sacramento soil moisture accounting model of the US National Weather Services river forecast system (SAC-SMA) known as the six-parameter model. It is shown that the fuzzy logic framework enables the decision maker to g ain insight about the model sensitivity and the uncertainty stemming from t he elements of the CRR model. (C) 2001 Elsevier Science Ltd. All rights res erved.