JINCan,YUANWen-xiu,ZHOUHong,et al.Application of Partitioning Clustering in Short-duration Storm Pattern Design[J].China Water & Wastewater,2024,40(3):113-119.
Application of Partitioning Clustering in Short-duration Storm Pattern Design
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第40卷
Number:
第3期
Page:
113-119
Column:
Date of publication:
2024-02-01
- Keywords:
- design storm pattern; partitioning clustering; K-means clustering; PAM clustering; Leshan City
- Abstract:
- In order to better reflect the characteristics of short-duration rainstorms and classify the storm patterns, based on the minute-by-minute rainfall data from 1981 to 2021 at the National Benchmark Climate Station in Leshan, Sichuan Province, the best clustering numbers were selected for 90 rainstorms with 60 min duration, 78 rainstorms with 120 min duration and 86 rainstorms with 180 min duration using various methods. K-means and PAM partitioning clustering methods were used to analyze the storm pattern. The results showed that K-means clustering was more intuitive and effective than PAM clustering in classifying urban short-duration storm patterns. The storm patterns of Leshan Climate Station with duration of 60 min, 120 min and 180 min were divided into three categories, two categories and three categories respectively, and the storm patterns with a single peak in front were the most common. The results are intended to provide references for sponge city construction, drainage and waterlogging control planning of Leshan City, and provide new ideas for the application of machine learning in the research of design storm patterns.
Last Update:
2024-02-01