Comparing rainfall prediction at various time scales and rainfall interpolation at the regional scale using artificial neural networks

Precipitation prediction is crucial for various sectors, including agriculture, water resource management, and transportation. This paper addresses the gap in rainfall prediction studies that seldom separate spatial and temporal scales. The spatial distribution of rainfall in Zhejiang Province in 2015 is analyzed using multiple interpolation methods: Inverse Distance to a Power (IDP), Kriging Interpolation, Minimum Curvature Interpolation (MC), Modified Shepard’s Method (MSM), Nearest Neighbour Interpolation (NeaN), and Natural Neighbor(NN). Additionally, daily and monthly rainfall are simulated using a 30-year historical precipitation dataset (1990-2020) of an observation station from Zhejiang Province, employing Radial Basis Function Network (RBFN) and Back Propagation (BP) neural networks.Our results confirm the accuracy of these methods in both simulation and interpolation. The benefits of Artificial Neural Networks (ANNs) over traditional interpolation techniques in predicting rainfall are underscored, while the challenges ANNs face, such as nonlinear rainfall patterns, training time, computational complexity, and the configuration of meteorological stations are also acknowledged. Notably, RBFN outperformed the BP model in simulating rainfall, especially for longer forecast periods.In conclusion, the potential applications and future directions of ANNs in rainfall prediction are discussed, their utility across different spatial and temporal scales are emphasized. By comparing ANNs with classical interpolation methods, their respective strengths are highlighted, providing scientific insights for future precipitation forecasting at provincial administrative levels.