Spatial and Temporal Pattern of Rainstorms Based on Manifold Learning Algorithm
Yuanyuan Liu,
Yesen Liu,
Hancheng Ren,
Longgang Du,
Shu Liu,
Li Zhang,
Caiyuan Wang,
Qiang Gao
Affiliations
Yuanyuan Liu
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Yesen Liu
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Hancheng Ren
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Longgang Du
Beijing General Hydrology Station, Beijing 100089, China
Shu Liu
State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
Li Zhang
Shenzhen National Climate Observatory, Shenzhen 518040, China
Caiyuan Wang
Beijing General Hydrology Station, Beijing 100089, China
Qiang Gao
Beijing General Hydrology Station, Beijing 100089, China
Identifying the patterns of rainstorms is essential for improving the precision and accuracy of flood forecasts and constructing flood disaster prevention systems. In this study, we used a manifold learning algorithm method of machine learning to analyze rainstorm patterns. We analyzed the spatial–temporal characteristics of heavy rain in Beijing and Shenzhen. The results showed a strong correlation between the spatial–temporal pattern of rainstorms and underlying topography in Beijing. However, in Shenzhen, the spatial–temporal distribution characteristics of rainstorms were more closely related to the source of water vapor causing the rainfall, and the variation in characteristics was more complex and diverse. This method may be used to quantitatively describe the development and dynamic spatial–temporal patterns of rainfall. In this study, we found that spatial–temporal rainfall distribution characteristics, extracted by machine learning technology could be explained by physical mechanisms consistent with the climatic characteristics and topographic conditions of the region.
