CAJ | 학술논문

利用海南省1992—2011年台风灾害损失数据，采用主成分分析法确定了台风灾害损失的致灾因子及灾情指标因子。借助广义动态模糊神经网络（GD—FNN）在学习过程中逐渐形成模糊规则的优点，设计了一种模糊神经网络模型并应用于台风灾害损失的预测预警中，定量地研究了台风灾害致灾因子与灾情指标因子之间的规律。训练结果表明实际样品值与训练结果拟合较平滑，受台风影响较大的局部降雨量峰值增强了间接经济损失预测值。将建立的预测模型应用201119“尼格”台风的灾害损失预测，实验结果表明该模型能较好地预测台风灾害中倒塌房屋、受灾农作物面积、受灾人口及间接经济损失。
이용해남성1992—2011년태풍재해손실수거，채용주성분분석법학정료태풍재해손실적치재인자급재정지표인자。차조엄의동태모호신경망락（GD—FNN）재학습과정중축점형성모호규칙적우점，설계료일충모호신경망락모형병응용우태풍재해손실적예측예경중，정량지연구료태풍재해치재인자여재정지표인자지간적규률。훈련결과표명실제양품치여훈련결과의합교평활，수태풍영향교대적국부강우량봉치증강료간접경제손실예측치。장건립적예측모형응용201119“니격”태풍적재해손실예측，실험결과표명해모형능교호지예측태풍재해중도탑방옥、수재농작물면적、수재인구급간접경제손실。
In this paper the typhoon disaster loss data from 1992 to 2011 in H ainan Province are used for statistical analysis and hazard and disaster indicators factor are determined by means of principal component analysis. With the GI〉FNN gradually formed in the process of learning the advantages of fuzzy rules, a fuzzy neural network model is designed and applied to the typhoon disaster loss forecast, and the law of the typhoon disaster hazard factor with disaster indicators factors is studied using Quantitative method. The training results show that the actual data value relatively fits the results but local rainfall peak enhances the predictive value of indirect economic losses. The prediction model is applied to the forecast of ＂Nalgae＂ Typhoon disaster loss, with the experimental result that the model has a better ability to predict typhoon disaster loss.