摘要
采用黑龙江省1984—2019年各县强降水灾情资料和逐日降水资料,以逻辑回归和长短时记忆网络模型为基础,建立了黑龙江全省、大兴安岭、小兴安岭、松嫩平原、三江平原和东南半山区的强降水致灾与否二分类预估模型。通过机器学习,得到黑龙江省以及5个地区判断强降水致灾与否的最佳观测天数在4~6 d、最佳的日降水量阈值为16~20 mm。比较全连接逻辑回归模型、优先考虑日期的部分连接逻辑回归模型D、优先考虑站点的部分连接逻辑回归模型S和长短时记忆网络LSTM模型等四个模型的表现,前三种逻辑回归模型表现差距不大,相对表现最好的全连接模型,其在大部地区所表现的准确率、精确率、召回率和F1分数均在0.7以上,而LSTM模型只在大兴安岭表现更好一些。
By utilizing heavy rainfall disaster and daily precipitation data from various counties in Heilongjiang Province from 1984-2019,and by using logistic regression and Long Short Term Memory Network(LSTM),disas⁃ter prediction models for heavy rainfall are established in Heilongjiang Province,including the Da Xing'an Moun⁃tains,the Xiao Xing'an Mountains,the Songnen Plain,the Sanjiang Plain,and the southeastern half mountainous area.Through machine learning,it is found that the optimal observation days for judging whether heavy rainfall caused disasters in Heilongjiang Province and 5 regions are 4~6 days,and the optimal daily precipitation threshold is 16~20 mm.Comparing the performance of four models which are the fully connected logistic regression model,the partially connected logistic regression model D that prioritized dates,the partially connected logistic regression model S that prioritized sites,and the LSTM model,the first three logistic regression models showed little difference in per⁃formance,with the fully connected model performing the best in comparison.The accuracy,precision,recall,and F1 scores of the fully connected logistic regression model in most regions are all above 0.7.The LSTM model only out⁃performed the logistic regression models in the Da Xing'an Mountains.
作者
李昊宸
邵源铭
杨洪伟
蒋慧亮
徐永清
李亚滨
魏磊
LI Haochen;SHAO Yuanming;YANG Hongwei;JIANG Huiliang;XU Yongqing;LIYabin;WEI Lei(Harbin Institute of Technology,Harbin 150096,China;Heilongjiang Climate Center,Harbin 150030,China)
出处
《灾害学》
CSCD
北大核心
2024年第3期60-65,共6页
Journal of Catastrophology
基金
中国气象局决策服务专题研究重点项目“中国气象局决策服务专题研究重点项目”(JCZX2022002)。
关键词
机器学习
逻辑回归模型
长短时记忆网络模型
强降水致灾预估模型
黑龙江
machine learning
logistic regression model
long short term memory network model
disaster prediction model caused by heavy rainfall
Heilongjiang
