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Low-visibility forecasting method based on multi-neural network successive approximation method

A neural network and low-visibility technology, applied in the field of meteorology, can solve problems such as poor forecasting accuracy, achieve accurate forecasting, improve forecasting accuracy, and high efficiency

Pending Publication Date: 2018-11-02
天津市气象科学研究所
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Problems solved by technology

[0005] In view of this, the present invention aims to propose a low-visibility forecast method based on multiple neural network step-by-step approximation methods to solve the problem that existing visibility forecasts can only provide qualitative forecasts for foggy days and non-foggy days, and the forecast accuracy is limited. worse case

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[0038] It should be noted that, in the case of no conflict, the embodiments of the present invention and the features in the embodiments can be combined with each other.

[0039] In describing the present invention, it should be understood that the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", " The orientations or positional relationships indicated by "vertical", "horizontal", "top", "bottom", "inner" and "outer" are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and Simplified descriptions, rather than indicating or implying that the device or element referred to must have a particular orientation, be constructed and operate in a particular orientation, and thus should not be construed as limiting the invention. In addition, the terms "first", "second", etc. are used for descriptive purposes only, and should not be understood ...

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Abstract

The invention provides a low-visibility forecasting method based on a multi-neural network successive approximation method. A forecasting factor with a clear physical significance is selected; and byutilizing meteorological data, in combination with a neural network model algorithm, a low-visibility forecasting model is built. The low-visibility forecasting method based on the multi-neural network successive approximation method is better in effect and high in efficiency, and effectively improves the forecasting precision; a neural network model is adopted, so that higher growth is achieved;and the forecasting can be more accurate along with the increase of subsequent samples.

Description

technical field [0001] The invention belongs to the technical field of meteorology, and in particular relates to a low-visibility forecasting method based on a multiple neural network step-by-step approximation method. Background technique [0002] Low visibility has a great impact on port economic production and traffic safety, and is a weather phenomenon of great concern to port disaster prevention and mitigation. The emergence of low visibility is related to weather phenomena such as fog, precipitation, haze and sand dust, among which heavy fog is the most important weather phenomenon affecting port visibility. According to statistics, 80% of sea collisions are caused by poor visibility caused by fog . Fog has obvious regional and seasonal characteristics, its formation mechanism is complex, and its forecast is difficult. Therefore, the research on the forecast of heavy fog has always been a difficult point and hot spot in meteorological forecasting in recent years. [...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G01W1/10
CPCG06N3/08G01W1/10G06N3/045
Inventor 吴彬贵李英华张建春王亚男陈靖王雪莲邱晓滨
Owner 天津市气象科学研究所
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