| 不确定条件下内河航道通航环境风险评价 |
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| 引用本文： | 邱文钦,唐存宝,唐强荣. 不确定条件下内河航道通航环境风险评价[J]. 中国航海, 2019, 0(1): 52-55,67 |
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| 作者姓名： | 邱文钦 唐存宝 唐强荣 |
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| 作者单位： | 广州航海学院海运学院 |
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| 基金项目： | 广东省交通运输厅科技计划项目(2012-02-045) |
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| 摘 要： | 在船舶大型化趋势明显,船舶交通量迅猛增长的背景下,如何保障船舶进港航道通航环境,确保船舶安全、有效进港成为众多研究关注的焦点。结合高斯混合聚类模型(Gaussian Mixture Model, GMM)和概率神经网络(Probabilistic Neural Network, PNN)的特点,构建在不确定条件下的基于GMM-PNN模型的内河航道通航环境风险评价模型。案例分析表明:所提出的模型具有较强的应用性和普适性,能够为有关部门实施现代化海事监管提供夯实的理论基础。
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| 关 键 词： | 通航环境 高斯混合聚类 概率神经网络 风险评价 不确定性 |
Navigation Environment Risk Assessment of Uncertain Inland Waterway |
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| QIU Wenqin,TANG Cunbao,TANG Qiangrong. Navigation Environment Risk Assessment of Uncertain Inland Waterway[J]. Navigation of China, 2019, 0(1): 52-55,67 |
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| Authors: | QIU Wenqin TANG Cunbao TANG Qiangrong |
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| Affiliation: | (Navigation Department,Guangzhou Maritime Institute, Guangzhou 510725, China) |
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| Abstract: | Navigation safety problems are arising with the rapid development of big-sized ships and serious traffic jam in inland waterways. Based on GMM-PNN(Gaussian Mixture Model and Probabilistic Neural Network), a model of navigation environment risk assessment of uncertain inland waterway is analyzed. Case studies indicate that the proposed model is practical and applicable, providing a theoretical reference to modern inland waterway supervision. Case studies indicate that the proposed model is practical and generally applicable. |
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| Keywords: | navigation environment GMM PNN risk assessment uncertainty |
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