Abstract: 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.

Key words: Heavy rainfall disaster, OCSVM, Correlation analysis, Disaster-causing factor, Reliability discriminant