Ocean single-element observation quality control method based on multi-model fusion

Abstract

An ocean single-element observation quality control method based on multi-model fusion adopts a four-layer model architecture combining statistical analysis and a single classification algorithm to detect whether historical observation data of a certain element of an ocean site is abnormal or not. The method comprises the following steps: S1, an input layer, which constructs three time windows from far to near for historical observation data of a certain element of a marine site, extracts statistical features, fits features and classification features, and constructs a detection sample; S2, a statistical analysis layer, which filters 70% of positive samples by using a statistical discrimination algorithm, reduces the scale of an abnormal candidate set, and effectively relieves the influence caused by imbalance of the positive and negative samples; S3, a single classification layer, which further detects the suspected abnormal observation data points by using a single classification model; and S4, the output layer, which is used for comprehensively making a final judgment according to results of the statistical analysis layer and the single classification layer, and evaluating a detection result. According to the method, the detection results of various models are comprehensively considered to make an optimal decision, so that the accuracy of the detection method is effectively improved.

Description

technical field [0001] The invention relates to the technical field of marine monitoring, and more specifically relates to a method for controlling the quality of marine single-element observation based on multi-model fusion. Background technique [0002] Marine environmental observation data is an important part of marine data, especially the long-term and continuous advantages of data such as moored buoys and ocean stations are unmatched by other navigation and large-scale survey data. The long-term observation data are affected by human and non-human factors during the collection process, as well as station site change, platform drift, instrument change, observation time / calculation method change, etc., resulting in a certain discrepancy between the ocean state represented by the observation data and the actual state. Bias, therefore, requires quality control of the data. [0003] At present, the monitoring of oceans at home and abroad still mainly adopts traditional tes...

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Problems solved by technology

[0007] The purpose of the present invention is to solve the problems of low detection efficiency and long-term and short-term correlation phenomenon existing in the existing marine data quality control technology, and to provide a marine single-element observation quality control method based on multi-model fusion. Use machine learning related technologies and integrated learning ideas to build a model for data quality, so as to control the data quality of marine observation stations

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