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Multiobjective optimization in water resources planning consists in trading off noncommensurable objectives within the framework of a complex and dynamic process. Multiobjective optimization is performed at two levels: first, an engineering level, which may be labeled a decision‐making aid phase; and then, a managerial level of acceptance of the solution. The engineering level optimization may be performed by means of a cost‐effectiveness approach followed by the application of compromise programing, which consists of choosing a compromise solution located as close as possible to an ideal but non‐feasible solution. In this paper this combined method is applied to the design of a water resources system in the Central Tisza River Basin in Hungary. The results obtained by following this approach are compared to those in David and Duckstein (1976) and Keeney and Wood (1977), who used, respectively ELECTRE and multiattribute utility theory instead of compromise programing to study the same basin. The proposed methodology is able to lead to either one of the two different decisions resulting from the other studies. A brief discussion of possible approaches for final choice of alternative system is given. Key words in this paper are multiobjective optimization, decision‐making aid, cost‐effectiveness approach, compromise programing, and river basin development.

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A general methodology for fuzzy regression is developed and illustrated by an actual hydrological case study. Fuzzy regression may be used whenever a relationship between variables is imprecise and/or data are inaccurate and/or sample sizes are insufficient. In such cases fuzzy regression may be used as a complement or an alternative to statistical regression analysis. In fuzzy regression, several "goodness of fit" criteria may be used such as the maximum average vagueness criterion and the prediction vagueness criterion. The technique is illustrated by means of a case study involving the relationship between soil electrical resistivity and hydraulic permeability. This relationship is imprecise and based on only a few data points. In the present case a curvilinear relationship is fitted using fuzzy regression with six calculated resistivities and six measured permeabilities. Prediction vagueness criteria appears to yield a more robust fuzzy regression than the maximum average vagueness criteria. Potential application areas of fuzzy regression in hydrology are discussed further. The methodology is relatively simple, and the results can be interpreted to provide a valuable hydrological decision-making aid.