周纳, 刘强. 基于模糊神经网络的广东省台风灾害损失预测[J]. 海洋环境科学, 2022, 41(3): 461-466. DOI: 10.12111/j.mes.2021-0047
引用本文: 周纳, 刘强. 基于模糊神经网络的广东省台风灾害损失预测[J]. 海洋环境科学, 2022, 41(3): 461-466. DOI: 10.12111/j.mes.2021-0047
ZHOU Na, LIU Qiang. Prediction of typhoon disaster losses in Guangdong province based on fuzzy neural networks[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2022, 41(3): 461-466. DOI: 10.12111/j.mes.2021-0047
Citation: ZHOU Na, LIU Qiang. Prediction of typhoon disaster losses in Guangdong province based on fuzzy neural networks[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2022, 41(3): 461-466. DOI: 10.12111/j.mes.2021-0047

基于模糊神经网络的广东省台风灾害损失预测

Prediction of typhoon disaster losses in Guangdong province based on fuzzy neural networks

  • 摘要: 台风灾害是我国最严重的海洋灾害之一,研究提高台风灾害损失预测的准确度对防灾减灾具有重要意义。针对目前机器学习算法在处理小样本数据时预测精度不高的问题,提出结合模糊数学的BP神经网络算法对台风灾害损失进行预测。本文选用广东省2005-2016年记录较为完善的25个台风样本数据进行实验,首先利用信息扩散技术对初始数据进行正态信息扩散,再结合BP神经网络对台风灾害损失进行预测。结果表明,该方法能较好地解决台风灾害实测样本少和存在矛盾样本的问题,提高了台风灾害损失预测的精度。

     

    Abstract: Typhoon disaster is one of the most serious marine disasters in China. Research on improving the accuracy of typhoon disaster loss prediction is of great significance to disaster prevention and mitigation.Aiming at the problem that the current machine learning algorithms have low prediction accuracy when processing small sample data, BP neural network algorithm combined with fuzzy mathematics is proposed to predict the loss of typhoon disasters. In this paper, 25 typhoon sample data with better records were selected for experiments in Guangdong province from 2005 to 2016. First, the initial data is diffused with normal information using the information diffusion technology, and then combined with BP neural network to predict the typhoon disaster loss.The results show that this method can solve the problem of fewer measured samples and contradictory samples of typhoon disaster and improve the accuracy of typhoon disaster loss prediction.

     

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