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利用人工神经网络模型预测西北太平洋热带气旋生成频数

海滢 陈光华

海滢, 陈光华. 利用人工神经网络模型预测西北太平洋热带气旋生成频数[J]. 气候与环境研究, 2019, 24(3): 324-332. doi: 10.3878/j.issn.1006-9585.2019.18110
引用本文: 海滢, 陈光华. 利用人工神经网络模型预测西北太平洋热带气旋生成频数[J]. 气候与环境研究, 2019, 24(3): 324-332. doi: 10.3878/j.issn.1006-9585.2019.18110
Prediction of Frequency of Tropical Cyclones Forming over the Western North Pacific Using An Artificial Neural Network Model[J]. Climatic and Environmental Research, 2019, 24(3): 324-332. doi: 10.3878/j.issn.1006-9585.2019.18110
Citation: Prediction of Frequency of Tropical Cyclones Forming over the Western North Pacific Using An Artificial Neural Network Model[J]. Climatic and Environmental Research, 2019, 24(3): 324-332. doi: 10.3878/j.issn.1006-9585.2019.18110

利用人工神经网络模型预测西北太平洋热带气旋生成频数

doi: 10.3878/j.issn.1006-9585.2019.18110
基金项目: 国家自然科学基金项目 41775063、41475074,国家重点研发计划项目2017YFC1501901

Prediction of Frequency of Tropical Cyclones Forming over the Western North Pacific Using An Artificial Neural Network Model

Funds: National Natural Science Fundation of China (Grants 41775063 and 41475074), National Key Research and Development Program of China Grant 2017YFC1501901National Natural Science Fundation of China (Grants 41775063 and 41475074), National Key Research and Development Program of China (Grant 2017YFC1501901)
  • 摘要: 通过对60年(1950~2009年)北半球夏、秋季(6~10月)热带气旋(TC)频数与春季(3~5月)大尺度环境变量的相关分析,挑选出8个相关性较高的前期预报因子建立人工神经网络(ANN)模型,对2010~2017年8年夏、秋季TC频数进行回报,并将回报结果与传统多元线性回归(MLR)方法所得结果进行对比分析。结果表明,ANN模型对60年历史数据的拟合精度高,相关系数高达0.99,平均绝对误差低至0.77。在8年回报中,ANN模型相关系数为0.80,平均绝对误差为1.97;而MLR模型相关系数仅为0.46,平均绝对误差为3.30。ANN模型在历史数据拟合和回报中的表现都明显优于MLR模型,未来可考虑应用于实际的业务预测中。
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  • 收稿日期:  2018-08-11

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