Assessing the Forecasting Accuracy of a Modified Grey Self-Memory Precipitation Model Considering Scale Effects

Precipitation is an important parameter in water resource management, urban flood warning systems, and hydrological analyses. forecasting can provide a decision-making basis for relevant organizations, such as those the agricultural sector conservancy departments. In this paper, modified grey self-memory model (MGSM) was constructed by combining function theory. To verify precision of cases which measured data are not available stage, self-test method based on scale effect precipitation stage proposed. Ultimately, verified three scales—the annual scale, crop growth period, monthly scale—in period from 1961 to 2018 Songnen Plain area, Heilongjiang Province. The results showed that MGSM yielded higher fitting accuracy than original GM(1,1) models. Furthermore, study area predicted with MSGM at different scales above 2019 2023. meets requirements, be used forecast trends time future. reference formulating scientific rational use strategies guiding production practices.