基于机器学习的雨型分类研究:以淠河流域为例OACSTPCD

[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.