CAJ | 학술논문

目的对广东省气象观测数据挖掘分析，以广东省农业气象灾害中的高温为例，预测可能存在的灾害及其等级。方法在缺乏灾害判定规则和历史灾情等先验知识的条件下，应用模糊C均值聚类算法（ FCM）挖掘得出关键属性的聚类中心和隶属度矩阵，建立灾害等级判定规则，进而通过气象观测数据预测可能即将发生的农业气象灾害及其等级。通过误差反向传播（ BP）神经网络算法对气象观测历史数据及同期发布的灾害等级数据进行学习，训练后的网络模型可以准确地揭示内在的灾害发生规律，进而通过气象观测数据精确地预测可能即将发生的农业气象灾害及其等级。结果和结论 BP和FCM 2种数据挖掘方法在缺乏先验知识的条件下，均可以通过气象观测数据准确预测农业气象灾害，结果对比表明前者预测气象站点灾害等级的精度略优于后者。
목적대광동성기상관측수거알굴분석，이광동성농업기상재해중적고온위례，예측가능존재적재해급기등급。방법재결핍재해판정규칙화역사재정등선험지식적조건하，응용모호C균치취류산법（ FCM）알굴득출관건속성적취류중심화대속도구진，건립재해등급판정규칙，진이통과기상관측수거예측가능즉장발생적농업기상재해급기등급。통과오차반향전파（ BP）신경망락산법대기상관측역사수거급동기발포적재해등급수거진행학습，훈련후적망락모형가이준학지게시내재적재해발생규률，진이통과기상관측수거정학지예측가능즉장발생적농업기상재해급기등급。결과화결론 BP화FCM 2충수거알굴방법재결핍선험지식적조건하，균가이통과기상관측수거준학예측농업기상재해，결과대비표명전자예측기상참점재해등급적정도략우우후자。
Objective]To forecast agrometeorological disasters and their levels as an example of high te-mperature disaster in Guangdong Province .[Method] Due to lack of disaster decision rule and historical disaster level data , high temperature disaster level rules were built using fuzzy clustering algorithm ( FCM) based on the meteorological data in the long term .Those rules were concluded from the cluster centers of the key attribute and membership degree matrix according to the maximum membership degree principle .Based on these rules , possible disasters and their levels were predicted by dynamic meteoro-logical data .The back propagation network algorithm ( BP) in the absence of disa-ster decision rules was applied to study historical meteorological observation data and synchronous disaster level released by the meteorological bureau .The trained BP network models were accurate to discover the inner rules of disas-ters, so the BP network models were fit for predicting the possible disasters and their level through dynamic observation of data at many meteorological stations .[Result and conclusion] Comparing the re-sults of the two methods of data mining , the neural network is found slightly better than the fuzzy cluste-ring to predict the meteorological disaster level .