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首页> 外文期刊>Journal of Hydroinformatics >Multi-objective optimization of wave break forest design through machine learning
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Multi-objective optimization of wave break forest design through machine learning

机译：通过机器学习对防波堤森林设计进行多目标优化

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摘要

Planting trees on a floodplain along a river is a practical and ecological method for embankment protection. Optimization of wave break forest is also a new concept on wave attenuation studies. In this study, we carried out physical experiments to obtain fundamental data and proposed the Cluster Structure Preserving Based on Dictionary Pair for Unsupervised Feature Weighting model (CDUFW) for multi-objective wave break forest design. Physical experiments were designed with considering the effects of different planting configurations on wave attenuation in three scenarios: (1) the equilateral triangle arrangement with different row spacings; (2) different arrangements with the same density; (3) different tree shapes with the same row spacing. The physical experiment condition was typically defined according to the field research of the study area. Then, a multi-objective weighting model for wave break forest design optimization was based on the scheme set of physical experiment outputs using the proposed CDUFW model. Physical experiments showed that different arrangement modes take advantage of the wave attenuation effect of different forest widths. The CDUFW model performed well in finding the effective, economic and reasonable scheme. The proposed model is excellent in data mining and classification, and can be applied to many decision-making and evaluation fields.
机译：在沿河的洪泛区上植树是一种实用且生态的堤防保护方法。防波林的优化也是波浪衰减研究的新概念。在这项研究中，我们进行了物理实验以获取基础数据，并提出了基于字典对的聚类结构保留用于多目标防波堤设计的无监督特征加权模型（CDUFW）。设计物理实验时考虑了三种情况下不同种植方式对波浪衰减的影响：（1）具有不同行距的等边三角形排列； （2）密度相同的不同布置； （3）具有相同行距的不同树形。通常根据研究区域的现场研究来定义物理实验条件。然后，使用所提出的CDUFW模型，基于物理实验输出的方案集，建立了一个用于破碎林设计优化的多目标加权模型。物理实验表明，不同的布置方式可以利用不同林宽的波衰减效应。 CDUFW模型在找到有效，经济和合理的方案方面表现良好。所提出的模型在数据挖掘和分类方面表现优异，可应用于许多决策和评估领域。

著录项

  • 来源
    《Journal of Hydroinformatics》 |2019年第2期|295-307|共13页
  • 作者

    Ren Jie; Dong Zengchuan; Xu Wei; Zhang Qilai; Shi Rensheng; Zhu Hansong; Sun Biao;

  • 作者单位

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    Tianjin Univ, Sch Comp Sci & Technol, 135 Yaguan Rd, Tianjin 300350, Peoples R China;

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China;

    Hohai Univ, Coll Hydrol & Water Resources, 1 Xikang Rd, Nanjing 210098, Jiangsu, Peoples R China|Water Resources Dept Heilongjiang Prov, 4 Wenzhong Rd, Harbin 150001, Heilongjiang, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    dictionary learning; physical experiment; spectral clustering; vegetation; wave attenuation;

    机译：字典学习;物理实验;光谱聚类;植被;波衰减;

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