Method for screening optimum indexes of reservoir flood control dispatching scheme based on BP neural network

Abstract

The invention discloses a method for screening the optimum indexes of a reservoir flood control dispatching scheme based on a BP neural network, comprising the following steps: generating network training samples; determining the topology of the BP neural network; training and inspecting the BP neural network; identifying the importance of each index in the BP neural network; calculating the degree to which the change of each index value affects the result, and analyzing the sensitivity of each index value; and determining the criterion and threshold of index screening. A lot of network training samples are generated through an index uniform discretization method, which effectively guarantees the training precision of the BP neural network. Based on an information storage and conversion mechanism of the BP neural network, the indexes and the influence of relative change of the indexes to the result of reservoir flood control dispatching scheme assessment are quantitatively evaluated according to the relative degree of importance and relative rate of contribution. By establishing a comprehensive judgment index, index screening is converted from a subjective process of analysis and judgment into a quantitative process of analysis and calculation.

Description

technical field [0001] The invention relates to reservoir flood control scheduling technology, in particular to a method for screening optimal indicators of reservoir flood control scheduling schemes. Background technique [0002] Reservoir flood control and dispatching technology is an important non-engineering measure to realize flood control and disaster reduction in a river basin, and is closely related to social, economic, natural, ecological and other factors. At present, the more common practice is to generate a set of feasible schemes through the flood control dispatching model, and the decision maker chooses a satisfactory scheme that comprehensively weighs the benefits of each target and puts it into decision-making. . [0003] Existing methods have relatively subjective arbitrariness in the selection of indicators, generally lack the identification process of the relative importance and sensitivity of indicators, and the criteria for indicator selection and the c...

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Problems solved by technology

[0003] Existing methods have a lot of subjective arbitrariness in the selection of indicators, generally lack the identification process of the relative importance and sensitivity of indicators, and the criteria for indicator selection and the corresponding threshold problems have not been effectively resolved.

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