Wetland early warning method based on artificial intelligence and random adaptive threshold

Abstract

The invention discloses a wetland early warning method based on artificial intelligence and a random self-adaptive threshold, and the method builds a random self-adaptive parameter learning module through employing a loop iteration principle, and enables an early warning system to have a self-adaptive adjustment function. Numerous uncertainties faced by a wetland water system are considered, an uncertainty quantification theory (random) is applied to construction of a random adaptive threshold, concepts such as a normal threshold parameter probability density function (PDF) and a normal threshold parameter cumulative distribution density function (CDF) are used for quantifying random uncertainty, and an artificial intelligence algorithm is used for ensuring the reaction efficiency of real-time early warning. Residual analysis of the prediction data and the observation data is used as index data for monitoring the wetland ecological state, so that the early warning system is more sensitive; the introduction of Bayesian reduces the false alarm rate, and introduces the iterative early warning rate. Therefore, the accuracy of wetland health early warning is comprehensively improved.

Description

technical field [0001] The invention relates to the technical field of ecological environment, in particular to a wetland early warning method based on artificial intelligence and random self-adaptive threshold. Background technique [0002] Wetland is a unique ecosystem formed by the interaction of water and land on the earth. It is an important living environment and one of the most biologically diverse ecological landscapes in nature. Pollution and other aspects play an important role, and it is known as "the kidney of the earth", "the cradle of life", "the birthplace of civilization" and "the gene pool of species". Wetlands and the various resources that coexist with them are an important basis for human survival and sustainable development, have an irreplaceable role in environmental regulation, and are extremely important species gene pools. For this reason, early warning systems and methods for wetlands came into being. [0003] However, most of the existing early w...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art, provide a self-adaptive adjustment capability, consider the many uncertainties faced by the wetland water system, solve the problems of over-sensitive early warning system, too slow static early warning, and accurate early warning. Highly accurate wetland early warning method based on artificial intelligence and random adaptive threshold

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