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In order to further grasp the scientific method of forecasting the spreading trend of forest fires in Heilongjiang Province, which is located in Northeast China, the basic concepts of forest fires, a geographical overview of Heilongjiang Province, and an overview of forest fire forecasting are mainly introduced. The calculation and computer simulation of various forest fire spread models are reviewed, and the selected model for forest fires spread in Heilongjiang Province is mainly summarized. The research shows that the Wang Zhengfei–Mao Xianmin model has higher accuracy and is more suitable for the actual situation of Heilongjiang Province. However, few studies over the past three decades have updated the formula. Therefore, this empirical model is mainly analyzed in this paper. The nonlinear least squares method is used to re-fit the wind speed correction coefficient, which gets closer results to the actual values, and the Wang Zhengfei–Mao Xianmin model is rewritten and evaluated for a more precise formula. In addition, a brief overview of the commonly used Rothermel mathematical–physical model and the improved ellipse mathematical model is given, which provides a basis for the improvement of the forest fires spread model in Heilongjiang Province.

In this paper, the numerical lightning prediction G model (abbreviated as LNP-G1) established by the cumulus electric field model breaks through the traditional statistical prediction model of experience plus linear extrapolation, and adopts the dynamic model based on the objective development law of cumulus cloud for the first time, thus realizing the lightning prediction in the real sense.It is certain that with the application of LNP-G in business, it will provide timely and advanced lightning objective numerical forecast for marine ships, aerospace, rocket launch, forest fire prevention and other industries, so as to avoid or reduce the major harm caused by lightning.

Objective To explore the effects of daily mean temperature (°C), average daily air pressure (hPa), humidity (%), wind speed (m/s), particulate matter (PM) 2.5 (μg/m3) and PM10 (μg/m3) on the admission rate of chronic kidney disease (CKD) patients admitted to the Second Affiliated Hospital of Harbin Medical University in Harbin and to identify the indexes and lag days that impose the most critical influence. Methods The R language Distributed Lag Nonlinear Model (DLNM), Excel, and SPSS were used to analyze the disease and meteorological data of Harbin from 01 January 2010 to 31 December 2019 according to the inclusion and exclusion criteria. Results Meteorological factors and air pollution influence the number of hospitalizations of CKD to vary degrees in cold regions, and differ in persistence or delay. Non-optimal temperature increases the risk of admission of CKD, high temperature increases the risk of obstructive kidney disease, and low temperature increases the risk of other major types of chronic kidney disease. The greater the temperature difference is, the higher its contribution is to the risk. The non-optimal wind speed and non-optimal atmospheric pressure are associated with increased hospital admissions. PM2.5 concentrations above 40 μg/m3 have a negative impact on the results. Conclusion Cold region meteorology and specific environment do have an impact on the number of hospital admissions for chronic kidney disease, and we can apply DLMN to describe the analysis.