Analysis of the Simulation Performances of Precipitation Statistical Forecasting Models

The performance of a precipitation forecast model is related to many factors. In addition to research areas and research data characteristics, it is also affected by the model's algorithm, statistical simulation methods, and performance metrics. This paper is based on the daily rainfall, average temperature, and average relative humidity of 28 stations in Heilongjiang Province in China from 2015 to 2019, using Monte Carlo statistical simulation methods such as Hold-out, Bootstrap, and machine learning methods. For the first time, this paper systematically studied the performances of daily precipitation forecast models in Heilongjiang Province in the summer and the spatial distribution characteristics of the model performances. The results show that for the entire study area, the overall prediction performance of a BP (Back Propagation) neural network and support vector machine is not significantly different, and the value of the area under ROC cuvre is higher than 76%, which is significantly better than that of the decision tree. The prediction performance of the model estimated by Bootstrap is always better than that of Hold-out, and it helps improve the fidelity of the evaluation results. For a single station in the study area, except for certain stations, the value of accuracy and the area under ROC cuvre of the support vector machine are higher than 80%, and the spatial distribution trend is larger in the southeast and smaller in the northwest. This trend is basically consistent with the distribution of precipitation frequency. The overall prediction effect of the SVM (Support Vector Machine) model is better in the Xiaokingan and Zhangguangcai Mountains, followed by the Sanjiang and Songnen Plains. The sensitivity is higher in mountainous areas than in plain areas. The central and southern regions are larger, followed by the eastern region and then the western and northern regions. The spatial distribution of specificity is simply the opposite of that of sensitivity.