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The real disaster information is an important reference for preventing and reducing heavy rainfall disaster losses effectively. Taking process rainfall intensity as the index, this paper constructs a heavy rainfall disaster event database matching the multi-source meteorological disasters and disaster-causing processes in Hebei Province during 1984-2020. After manual quality control, 2305 groups of real disaster information and 263 groups of false information are obtained. In this study, correlation analysis was used to determine and select the rainfall eigenfactors that are significantly related to the disaster degree. Based on the One-class support vector machine (OCSVM) and 10 folds cross-validation method, 10 samples were randomly selected to establish the meteorological factor disaster discriminant model and test and optimize, so as to explore the intelligent and easy-to-use intelligent discriminant method of multi-source heavy rainfall disaster credibility. The results showed that: (1) there are eleven rainfall eigenfactors related to disaster degree at 0.01 significant level, which are maximum rainfall, minimum rainfall, average process rainfall, average daily rainfall, average hourly rainfall intensity, hourly maximum rainfall, 3-hour maximum rainfall, 6-hour maximum rainfall, 12-hour maximum rainfall, 24-hour maximum rainfall and rainfall in the first 10 days. (2) Ten models (M1-M10) were established using 11 rainfall eigenfactors. According to the identification accuracy of the real disaster, the optimal model was determined to be M9, with the authenticity rate of 96.4% and the falsification rate of 67.6%, which indicated that the model is one-sided in determining the authenticity of the disaster situation and should be further optimized. (3) Through the autocorrelation test, maximum rainfall, average hourly rainfall intensity, hourly maximum rainfall and rainfall in the first 10 days were taken as input factors. Ten models (M11-M20) were reconstructed, and the optimal model is M20 with 96.2% proof rate and 82.9% false rate. Based on comprehensive analysis, the model established by 4 factors is more reliable than the model established by 11 factors. [ABSTRACT FROM AUTHOR]

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The research takes apples and apricots of Hebei province as examples, based on the temperature data of meteorological stations from 1974 to 2015, and calculates accumulated temperature of each region since January, to determine the three phenological periods that are susceptible to frost disasters every year: the starting and the ending of initial budding stage, flowering stage and young fruit stage. Then the research calculates the daily minimum temperature of three phenological periods at different return periods based on the Gumbel distribution, which reveals the dangerous intensity of frost disaster, and evaluates the risk of production reduction due to frost disasters according to the vulnerability curve of the three phenological periods (the yield reduction rate corresponding to the daily minimum temperature) constructed from relevant literature. Taking the once-in-a-hundred-year return period as an example, apples and apricots in northern Hebei province were more likely to be affected by frost during the young fruit period and flowering period. Zhangjiakou city was most affected during each phenological period, with an average yield reduction rate of 47.44%. Among different periods, the risk of flowering period was the highest, the yield was reduced by 59.79%, in the flowering period, the risk of yield reduction of apples and apricots in Chengde city was up to 51.42%, while in the southern region of Hebei province, Xingtai city was relatively special, with the highest yield reduction rate at the initial budding stage, reaching 37.91%. According to the above evaluation results, targeted enhancements at frost disaster monitoring and early warning in each phenological period in these areas can be made. Adjust the planting structure properly, and speed up the development of new environmentally friendly forest and fruit anti-low temperature protection technologies. [ABSTRACT FROM AUTHOR]