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基于SSA-BP神经网络模型的风暴潮灾害损失评估
作者:贾丙宏1  祝文硕1  王瑞富1  高松2  胡莹1  王怀计1 
单位:1. 山东科技大学测绘与空间信息学院, 山东 青岛 266590;
2. 国家海洋局北海预报中心, 山东 青岛 266061
关键词:风暴潮 灾害损失评估 麻雀搜索算法 BP神经网络 
分类号:P731.23
出版年·卷·期(页码):2022·39·第二期(50-58)
摘要:
选取浙江省1990—2020年29组记录完整的风暴潮历史灾情资料,建立了系统的风暴潮灾害损失评估指标体系,使用灰色关联分析法对指标进行筛选预处理,并提出基于麻雀搜索算法优化的BP神经网络模型,对风暴潮灾害直接经济损失进行评估。结果显示:传统BP神经网络对训练集的拟合得到的R2值为0.771,而经麻雀搜索算法(SSA-BP)优化后的R2值达到0.916,且无论在稳定性还是精度方面均有所提高。
This paper selects 29 sets of complete historical disaster data of storm surge recorded from 1990 to 2020 in Zhejiang Province, and establishes a systematic disaster loss assessment indicators system for storm surge. Then, the indicators are screened and preprocessed using grey relational analysis method. Finally, this paper proposes a BP neural network model optimized by the sparrow search algorithm to evaluate the direct economic loss of storm surge disasters. The results show that the R2 value is 0.771 when the training set is fitted using the traditional BP neural network, while the R2 value reaches 0.916 after optimized by the sparrow search algorithm (SSA-BP) with improved stability and accuracy.
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