Precipitation Estimation Methods Based on BPNN and CNN

Abstract

The hydrological cycle in the natural environment plays a crucial role in influencing human societal progress and everyday life, particularly in the realm of agriculture. Precipitation is a vital component of the natural water cycle. In recent years, multiple approaches for estimating rainfall have been developed by researchers to achieve improved results. However, the precision of conventional rainfall estimation techniques remains inconsistent, particularly in instances of heavy rainfall, which can result in considerable errors. Scholars have turned their attention to deep learning techniques, which excel at processing raw data and autonomously identifying model parameters. In this study, we present and compare two deep learning frameworks for precipitation estimation based on BPNN and CNN, in contrast to traditional methods. We also use a real dataset to validate the effectiveness of the deep learning models, and the experimental outcomes indicate that the CNN-based precipitation estimation method outperforms several other models.