Based on the record data of water disasters and the 5-min precipitation of 40 meteorological observation stations along the Shuozhou-Huanghua Railway from 2017 to 2019, the characteristics of railway water disasters and precipitation distribution were analyzed, then the three precipitation factors including continuous precipitation, the hourly maximum precipitation and the 24-hour precipitation were counted, the rainfall warning thresholds of no warning, patrol warning, speed limit warning and blockade warning of railway sections in plains and mountainous areas were formulated by using the mean-standard deviation method and the maximum value method. The results show that the water disasters of the Shuozhou-Huanghua Railway mainly occurred in July and August, and the duration of precipitation was mostly within 48 hours. The precipitation types causing water disasters were mainly local rainstorm, short-time heavy precipitation and long-duration precipitation, the railway water disasters in plain sections were mainly caused by local rainstorm, but the main cause of mountainous sections was long-duration precipitation. For railway section in the plain, the accuracy rate of patrol warning was 88.5%, the false rate was 11.5%, the accuracy rate of speed limit warning was 100%, for the railway section in the mountainous, the accuracy of patrols warning was 88.9% and the false rate was 11.1%. The rainfall warning threshold for railway sections in plains and mountainous areas could provide reference for safe running and efficient operation of railway.

The method of road surface temperature prediction for 2022 winter Olympic demonstration station (Beijing Huilongguan station) was discussed based on the METRo model. The artificial thermal parameters of iterative training fitting with a large number of sample data were added to the METRo model as the leading parameters in order to reduce the systematic error of the METRo model and the influence of human production and life on road surface temperature prediction. The results are as follows: (1) After introduction of artificial heat parameters, the simulation ability of the METRo model was improved significantly , especially at night, the root mean square error of road surface temperature prediction could be controlled at about 1 ℃. (2) The effect of artificial heat on road surface temperature presented negative feedback during daytime and positive feedback at night. (3) The simulated road surface temperature still had some errors due to influence of limitation of radiation prediction ability of meteorological models. In the mass, it was feasible to simulate the road surface temperature, especially for low temperature of road surface at the winter Olympic expressway demonstration station by using the METRo model with pre-set anthropogenic thermal parameters, which could support the prediction and early warning ability of highway road surface temperature and road icing in winter.