Heavy fog identification system based on machine learning and routine meteorological observation and application method thereof

Abstract

A heavy fog identification system based on machine learning and routine meteorological observation comprises a user interaction module in a PC, a ground meteorological observation information processing module, a meteorological satellite remote sensing data processing module, a heavy fog identification training module based on machine learning, a heavy fog estimation module based on machine learning, a visibility rasterization module based on a RBF network, a heavy fog estimation checking module and an interface service module. The application method provided by the invention comprises 9 stepsof: performing real-time monitoring for the heavy fog of lattice points in a range of 1km and to provide data support for road closure control based on the local area agglomerate fog forecast by related departments. The heavy fog identification system based on machine learning and routine meteorological observation and the application method thereof can reduce a big amount of cost for arrangementof the visibility meters to effectively monitor the heavy fog (comprising agglomerate fog) to reduce the loss caused by diseases of the heavy fog (comprising agglomerate fog) so as to greatly ensurethe traffic road safety. Therefore, the heavy fog identification system based on machine learning and routine meteorological observation and the application method thereof have good application prospect.

Description

technical field [0001] The invention relates to the field of data analysis systems and application methods, in particular to a fog recognition system and application method based on machine learning and conventional meteorological observation. Background technique [0002] Fog is a weather phenomenon in which the horizontal visibility of the air is less than 200 meters. It reflects the furthest distance that the human eye can recognize objects through the atmosphere, and is also an important parameter that indirectly describes the degree of air pollution. Heavy fog mostly occurs in winter, which has a serious impact on traffic safety. In my country, heavy fog is one of the more common disastrous weathers that affect traffic. It has the characteristics of high probability of occurrence, wide range of occurrence, and high degree of harm. Judging from the national highway low-visibility disaster risk assessment results, all land areas in my country will occur, of which North C...

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the technical barriers existing in existing visibility instruments and meteorological satellites in monitoring foggy days, and the inability to effectively identify cloudy fog, which easily leads to traffic accidents, the present invention provides a method for effectively utilizing the observation data of conventional weather stations and the remote sensing data of stationary meteorological satellites. data, using machine learning technology to study the nonlinear relationship between visibility and conventional meteorological observation elements such as temperature, relative humidity, wind speed, wind direction, precipitation, and water vapor pressure (machine learning technology is the relevant processing software installed in the PC for each technology for learning and processing various kinds of data), using deep neural grid model of software in PC and based on big data technology to realize the estimation of visibility in foggy weather based on conventional meteorological observation data, and at the same time input multi-channel remote sensing information of stationary meteorological satellites into machine learning In the model, heavy fog monitoring can be quickly realized, and the update frequency of 1 to 30 minutes can be realized according to the demand, which can realize effective monitoring of local sudden fog, and provide relevant departments with road heavy fog based on more intensive visibility live distribution data. A fog recognition system and application method based on machine learning and conventional meteorological observations that bring convenience to weather control and effectively reduce traffic accidents

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