The model and method of efficiency estimation for disaster relief process under hydrological emergency of natural character on the basis of fuzzy logic have been offered.
It is determined that the implementation of a natural emergency can lead to disruption of the sustainable functioning of the city, region and country as a whole against the background of an constantly increasing human impact on the environment, climate change and other conditions. Natural hydrological emergencies, such as floods, levees, etc., are the most widespread in the world and affect a large number of people, especially the socially and economically disadvantaged part of urban population.
An analysis of previous research in this field has been carried out and it is determined that the problem under consideration cannot be fully formulated as a classical deterministic or probabilistic mathematical programming problem. This problem is characterized by significant uncertainty about the problem input variables. Two classes of such uncertainty are identified with the sources of origin - strategic and tactical.
The paper presents a formalized procedure for supporting managerial decision-making, which includes the steps of uncertainty consideration and parametric identification of a deterministic optimization model of resource support for the elimination of natural hydrological emergencies.
Linguistic variables were introduced to determine the parameters of an emergency, the parameters of the affected area, the characteristics of the technical arsenal of the territorial unit of the State Emergency Service of Ukraine, as well as the life support system. The Mamdani fuzzy inference algorithm was used to evaluate the effectiveness of disaster relief process. The software implementation of this model is executed in the open source information system Scilab, namely in SciFLT subsystem.
This model is the basis for further parametric identification and implementation of a deterministic model of resource support for disaster relief process that allows forming flexible management decisions.
Keywords: natural emergency, fuzzy logic, flood, resourcing