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基于随机森林的复坡堤越浪量预测研究

胡原野 王收军 陈松贵 柳叶 王家伟 田昀艳

胡原野,王收军,陈松贵,等. 基于随机森林的复坡堤越浪量预测研究[J]. 海洋学报,2021,43(10):1–9 doi: 10.12284/hyxb2021133
引用本文: 胡原野,王收军,陈松贵,等. 基于随机森林的复坡堤越浪量预测研究[J]. 海洋学报,2021,43(10):1–9 doi: 10.12284/hyxb2021133
Hu Yuanye,Wang Shoujun,Chen Songgui, et al. Overtopping prediction for composite slope breakwater based on random forest method[J]. Haiyang Xuebao,2021, 43(10):1–9 doi: 10.12284/hyxb2021133
Citation: Hu Yuanye,Wang Shoujun,Chen Songgui, et al. Overtopping prediction for composite slope breakwater based on random forest method[J]. Haiyang Xuebao,2021, 43(10):1–9 doi: 10.12284/hyxb2021133

基于随机森林的复坡堤越浪量预测研究

doi: 10.12284/hyxb2021133
基金项目: 国家自然科学基金(52001149,52039005,51861165102);中国科协青年人才托举工程(2018QNRC001);中央级公益性科研院所基本科研业务费(TKS20200204,TKS20210102,TKS20210110);天津市科技计划项目 (17PTYPHZ00080)
详细信息
    作者简介:

    胡原野(1995-),男,河南省许昌市人,主要从事波浪与结构物相互作用方向研究。E-mail:1140799473@qq.com

    通讯作者:

    陈松贵(1987-),男,天津市人,副研究员,主要从事波浪理论及波浪与结构物相互作用研究。E-mail:chensg05@163.com

  • 中图分类号: U661

Overtopping prediction for composite slope breakwater based on random forest method

  • 摘要: 针对复坡堤越浪量的计算问题,提出了采用随机森林算法预测越浪量的方法。首先,通过对欧洲CLASH数据集进行筛选,挑选出符合复坡堤越浪量预测的数据;其次,对数据做无量纲化处理,建立以随机森林为基础的复坡堤越浪量预测模型,并通过网格搜索(GridSearchCV)方法对模型进行调参以改善模型的性能;最后,利用决定系数${R^2}$来评估模型的精度,并将随机森林模型与集成神经网络模型做了预测能力的对比,同时还给出了随机森林模型各个特征参数对预测精度的重要性。结果显示,随机森林模型的决定系数为92.7%,集成神经网络模型的决定系数为87.7%,表明随机森林模型对越浪量具有更强的学习和预测能力。通过对特征重要性的分析,墙顶高程对模型预测精度的影响最大,堤顶高程次之,堤脚宽度影响最小。
  • 图  1  复坡堤参数示意图

    Fig.  1  Schematic diagram of composite slope breakwater parameters

    图  2  决策树基本结构示意图

    Fig.  2  Schematic diagram of the basic structure of the decision tree

    图  3  基于随机森林的复坡堤越浪量预测模型结构图

    Fig.  3  Structure diagram of overtopping prediction model of composite slope breakwater based on random forest

    图  4  训练集预测结果(随机森林)

    Fig.  4  Prediction result of training set (random forest)

    图  5  测试集预测结果(随机森林)

    Fig.  5  Prediction result of testing set (random forest)

    图  6  训练集预测结果比较(集成神经网络)

    Fig.  6  Prediction result of training set (ensemble neural network)

    图  7  测试集预测结果比较(集成神经网络)

    Fig.  7  Prediction result of testing set (ensemble neural network)

    图  8  模型特征参数重要性评价

    Fig.  8  Importance evaluation of model characteristic parameters

    表  1  无量纲化后输入参数分布特征

    Tab.  1  Distribution characteristics of input parameters after dimensionless

    特征参数平均值最大值最小值标准差
    ${H_{m{0 },t} }/{L_{m - 1,0,t} }$0.0330.0870.0050.012
    $\,\beta$0.71680.0000.0004.820
    $h/{L_{m - 1,0,t}}$0.1350.6660.0100.102
    ${h_t}/{H_{m0,t}}$3.46722.5660.4292.403
    ${B_t}/{L_{m - 1,0,t}}$0.0170.3960.0000.050
    ${h_b}/{H_{m0,t}}$0.1737.826–2.6521.014
    $B/{L_{m - 1,0,t}}$0.0930.9730.0000.109
    ${A_c}/{H_{m0,t} }$1.1554.216–5.2420.581
    ${R_c}/{H_{m0,t} }$1.2466.0320.0000.531
    ${G_c}/{L_{m - 1,0,t}}$0.0230.2570.0000.039
    $m$417.7551 050.00010.000455.659
    $\cot {\alpha _d}$1.6727.0000.0001.331
    $\cot {\alpha _{incl}}$2.58411.299–1.3312.096
    ${\gamma _f}$0.7901.0000.3800.269
    $D/{H_{m0,t}}$0.1180.8070.0000.152
    目标参数
    $q*$0.001 6680.1650.000 0010.010 84
    下载: 导出CSV

    表  2  重要参数取值范围

    Tab.  2  Value range of important parameters

    重要参数取值范围
    n_estimators10~200
    max_depth10~50
    max_featuresauto, sqrt
    下载: 导出CSV
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  • 收稿日期:  2020-06-27
  • 修回日期:  2021-04-09
  • 网络出版日期:  2021-07-13

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