Scenic-region-tourism-meteorological-disaster intelligent forecasting method based on difference correction

Abstract

The invention discloses a scenic-region-tourism-meteorological-disaster intelligent forecasting method based on difference correction. The scenic-region-tourism-meteorological-disaster intelligent forecasting method based on difference correction includes the steps that main meteorological factors of meteorological disasters occurring in a target scenic region are determined with the partial correlation analysis method, a difference set of historical data and numerical-forecasting-model output data of the main meteorological factors of the target scenic region is obtained, and a mapping relationship function set between the meteorological disasters and the main meteorological factors and a difference mapping relationship function set between the meteorological disasters and the main meteorological factors are calculated with the gene expression programming algorithm respectively; then a forecasting function set is subjected to difference superposition modification, and a forecasting model of the meteorological disasters of the target scenic region is obtained; all meteorological factor values output by a numerical forecasting model are substituted, and the possible occurring conditions of various meteorological disasters can be forecasted. By means of the scenic-region-tourism-meteorological-disaster intelligent forecasting method based on difference correction, the problems that in the prior art, the sample data requirements are high, the adaptive capacity is poor, and the calculation process is complex are solved, and a quite-good decision support can be provided for scenic-region-disaster controlling and management.

Description

technical field [0001] The invention relates to the field of weather forecasting. More specifically, the present invention relates to a method for intelligent prediction of tourist weather disasters in scenic spots based on difference correction. Background technique [0002] Tourism is an industry heavily dependent on the natural environment and meteorological conditions, and meteorological conditions are important factors affecting tourism safety and tourism quality. Tourism meteorological disasters have become an issue of increasing concern to meteorological departments and tourism management departments. How to correctly forecast and predict severe weather in and around scenic spots, how to provide accurate and effective tourism disaster warning services, and how to timely and effectively help tourists avoid tourism disasters It has become an urgent research topic to deal with risks and achieve safe and healthy travel, and to maximize the safety of people's lives and pr...

AI Technical Summary

Problems solved by technology

[0005] The present invention provides a method for intelligently predicting tourism meteorological disasters in scenic spots based on difference correction. The partial correlation analysis method is used to determine the main meteorological elements of the meteorological disasters in the target scenic spot, and the historical data and numerical forecast model output data of the main meteorological elements in the target scenic spot are obtained. difference set, and use the genetic expression programming algorithm to calculate the mapping relationship function set between the meteorological disaster and the main meteorological elements and the difference mapping relationship function set between the meteorological disaster and the main meteorological elements, and then make a difference on the prediction function set The prediction model of meteorological disasters in the target scenic area is obtained by superposition and correction, and finally the values ​​of various meteorological elements output by the numerical forecasting model are substituted to predict the possible occurrence of various meteorological disasters, which overcomes the high requirements for sample data in the prior art, Poor adaptability, complex calculation process and other deficiencies can provide good decision support for disaster prevention and management in scenic spots

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