Yellow River Water and Sand Monitoring Data Model based on SARIMAX Rediction

Abstract

With the evolution of history, the problems faced by the Yellow River Basin have been an important research topic in hydrology. In this paper, for the problems in the monitoring of water and sand in the Yellow River, a seasonal time series model of the monitoring data is established based on the time series prediction model, and the CUSUM algorithm is used to test the model. First, the data are processed to calculate the spearman correlation for each pair of categories. Then, for analyzing the description of single dimensional data, data descriptive analysis was used. Next, the data were forecasted using a seasonal time series forecasting model. Finally, the seasonal time series prediction model was used to predict water fluxes and solved using double interpolation to obtain the predicted effect plots for the next ten years. The proposed model is close to the reality, can reasonably solve the proposed problem, and is characterized by high practicality and high efficiency of the algorithm.