| 基于信息熵的水文站网优化准则的应用与评价 |
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| 引用本文： | 李禾澍,王栋,王远坤.基于信息熵的水文站网优化准则的应用与评价[J].水科学进展,2020,31(2):224-231. |
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| 作者姓名： | 李禾澍 王栋 王远坤 |
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| 作者单位： | 南京大学地球科学与工程学院,江苏南京210023;南京大学地球科学与工程学院,江苏南京210023;南京大学地球科学与工程学院,江苏南京210023 |
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| 基金项目： | 国家重点研发计划资助项目2016YFC0401501国家自然科学基金资助项目41571017 |
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| 摘 要： | 将基于信息熵的四类水文站网优化准则,即熵-互信息(H-T)准则、联合熵-总相关(H-C)准则、联合熵-互信息-总相关(H-T1-C/H-T2-C)准则以及信息传递指数(TI)准则,应用于太湖西南山丘区雨量站网,对四类准则进行对比和评价。以2007—2016年日降水序列为样本,根据各优化准则,分别采用3种数值离散化方法计算站点秩次,分析秩次相关性、对多目标准则中指标权重的敏感性以及秩次年际变化。结果表明,H-C准则对应秩次的代表性最好,对指标权重的敏感度最低,秩次年际变化小;H-T2-C准则对指标权重最敏感,秩次年际变化显著。H-C准则有利于反映基于信息熵的基本优化原则(增大信息量和降低冗余度),而H-T2-C准则有利于体现决策偏好。
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| 关 键 词： | 信息熵 水文站网优化 数值离散化 多目标优化 |
| 收稿时间： | 2019-05-22 |
Application and assessment of entropy-based criterions for hydrometric network optimization |
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| LI Heshu,WANG Dong,WANG Yuankun.Application and assessment of entropy-based criterions for hydrometric network optimization[J].Advances in Water Science,2020,31(2):224-231. |
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| Authors: | LI Heshu WANG Dong WANG Yuankun |
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| Affiliation: | School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China |
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| Abstract: | This study applied four classes of entropy-based optimization criterions for hydrometric networks, i.e., entropy-transinformation (H-T), entropy-total correlation (H-C), entropy-transinformation-total correlation (H-T1-C/H-T2-C), and transinformation index (TI), to a rainfall monitoring network in the southwest hilly area in the Taihu Lake Basin, and made comparison and assessment of the criterions. Using daily precipitation of 2007—2016 as samples, we calculated the stations' ranks with three data discretization methods respectively, then made correlation analysis of ranks, sensitivity analysis concerning the index weight of multi-objective criterions, and investigated the inter-annual variance of ranks. Results showed that ranks obtained with H-C was the most representative, the least sensitive to the index weight, and had insignificant inter-annual variance; H-T2-C was the most sensitive to the index weight, with significant inter-annual variance. H-C criterion can best reflect the basic entropy based optimization principles (increasing information content and reducing redundancy), while H-T2-C criterion can best reflect decision preferences. |
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| Keywords: | information entropy hydrometric network optimization data discretization multi-objective optimization |
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