| 基于遗传—神经网络方法的广西台风灾害评估模型研究 |
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| 引用本文： | 李艳兰,金龙,史旭明,陈丹.基于遗传—神经网络方法的广西台风灾害评估模型研究[J].气象与环境学报,2021,37(3):139-144. |
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| 作者姓名： | 李艳兰 金龙 史旭明 陈丹 |
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| 作者单位： | 1. 广西壮族自治区气候中心, 广西 南宁 5300222. 桂林航天工业学院, 广西 桂林 5410043. 广西壮族自治区气象科学研究所, 广西 南宁 530022 |
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| 基金项目： | 国家自然科学基金(41565005);广西自然科学基金(2020GXNSFAA297122);广西自然科学基金(2018GXNSFAA281229) |
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| 摘 要： | 选取1981—2018年影响广西且灾情记录比较完整的86个台风样本，基于台风灾害伤亡人数、直接经济损失划分灾情等级，选取致灾因子，利用遗传算法与神经网络相结合的方法建立广西台风灾害评估模型。结果表明：选取的台风灾害致灾因子与台风灾情等级之间具有显著的相关性，构建的遗传—神经网络集合预报模型对台风灾情预估效果较好，训练样本拟合一致率为86.1%，测试样本预报准确率为71.4%，其中严重和较重的台风灾情等级预报结果与实况基本一致，较轻等级的预报准确率达83.3%。
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| 关 键 词： | 台风灾害 预评估 遗传—神经网络 人工智能 |
| 收稿时间： | 2020-07-15 |
Study on assessment model of typhoon disaster in Guangxi based on genetic-neural network method |
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| Yan-lan LI,Long JIN,Xu-ming SHI,Dan CHEN.Study on assessment model of typhoon disaster in Guangxi based on genetic-neural network method[J].Journal of Meteorology and Environment,2021,37(3):139-144. |
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| Authors: | Yan-lan LI Long JIN Xu-ming SHI Dan CHEN |
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| Institution: | 1. Guangxi Climate Center, Nanning 530022, China2. Faculty of Science, Guilin University of Aerospace Technology, Guilin 541004, China3. Guangxi Institute of Meteorological Science, Nanning 530022, China |
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| Abstract: | Using 86 typhoon cases that affected Guangxi with relatively complete disaster records from 1981 to 2018, the typhoon disaster was classified and the disaster causing factors were selected based on the number of casualties and direct economic losses.The assessment model of the typhoon disaster in Guangxi was established by combining genetic algorithm and neural network.The results show that there is a significant correlation between the selected disaster factors and the typhoon disaster grades.The genetic-neural network ensemble prediction model which is constructed has a good effect on the typhoon disaster prediction.The fitting consistency rate of training samples is 86.1%, and the prediction accuracy of test samples is 71.4%.Among them, the prediction results of severe and heavy typhoon disaster grades are generally consistent with the actual situation, and the prediction accuracy of lighter grades is 83.3%. |
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| Keywords: | Typhoon disaster Pre-assessment Genetic-neural network method Artificial intelligence |
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