水利水电技术（中英文）2024，Vol.55Issue(2)：101-111,11.DOI:10.13928/j.cnki.wrahe.2024.02.009
基于机器学习的雨型分类研究:以淠河流域为例
Research of rain patterns classification based on machine learning:A case study in Pi River Basin
摘要
Abstract
[Objective]In order to enhance the scientificity and accuracy of flood forecasting scheme,It is an effective technical approach to conduct rainfall pattern classification,formulate different rainfall pattern forecasting schemes,and implement opera-tional forecasting.[Methods]Based on hourly rainfall observation data from 37 rain gauge stations in the Pi River Basin during the period of year 2003-2021,the widely recognized Dynamic Time Warping(DTW)algorithm is employed for rainfall pattern classification,and it serves as the benchmark classification result.Subsequently,four machine learning method,namely decision tree(DT),long short-term memory neural network(LSTM),LightGBM,and support vector machine(SVM),are selected to build classification models and evaluate their classification performances.By adjusting the sample size,the impact of different sample capacities on the classification effectiveness is analyzed.[Results]The result reveal that among the four classification models,LightGBM exhibites the highest accuracy and fastest training speed,while LSTM and SVM demonstrate good classifica-tion accuracy but relatively lower training efficiency,and DT exhibites relatively faster classification speed but lower accuracy.As the sample size increases,the classification result gradually stabilize,and the classification effectiveness and training efficiency of the four method improve gradually.[Conclusion]This result validate the strong applicability of machine learning method in rain-fall sequence pattern classification,providing technical support for the classification construction of flood forecasting schemes.关键词
降雨雨型/时空分布特征/动态时间规划/LightGBM/LSTM/降雨/机器学习Key words
rain patterns/spatial and temporal distribution characteristics/dynamic time planning/LightGBM/LSTM/rainfall/machine learning分类
建筑与水利引用本文复制引用
付晓娣,阚光远,刘荣华,梁珂..基于机器学习的雨型分类研究:以淠河流域为例[J].水利水电技术（中英文）,2024,55(2):101-111,11.基金项目
国家自然科学基金(42271095) （42271095）
中国水利水电科学研究院十四五"五大人才"计划(JZ0199A032021) （JZ0199A032021）
GHFUND A(ghfund202302018283) （ghfund202302018283）
城市水循环与海绵城市技术北京市重点实验室开放基金(HYD2020OF02) （HYD2020OF02）
