| 刘登峰,王栋,丁昊,王腊春.水体富营养化评价的熵-云耦合模型[J].水利学报,2014,45(10): |
| 水体富营养化评价的熵-云耦合模型 |
| Eutrophication assessment by Entropy-Cloud Model |
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| DOI： |
| 中文关键词: 信息熵 云模型 富营养化评价 水环境 层次分析法 |
| 英文关键词: information entropy cloud model eutrophication evaluation water environment AHP |
| 基金项目: |
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| 摘要点击次数: 2445 |
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| 中文摘要: |
| 引入熵和云模型理论，针对水体富营养化评价问题，提出了一种熵-云耦合评价模型。根据既定指标体系，确定相应于各富营养化等级的云模型参数；基于Shannon熵和层次分析法计算各指标的混合熵权，构建指标体系下的熵-云模型。代入水质资料，计算实测水体隶属于各富营养化等级的确定度，并引入确定度向量模糊熵作为第二维评价参量，用以表征水体富营养化的复杂度。运用熵-云模型对我国12个代表性湖库的富营养化程度进行了评价，评价结果与模糊可变集、神经网络、正态云模型等方法进行对比。与以往方法不同，熵-云耦合模型从等级和复杂度两个维向揭示了水体的富营养化程度，评价结果直观有效，可为水体富营养化评价工作提供新思路。 |
| 英文摘要: |
| An Entropy-Cloud Model was proposed to deal with the eutrophication assessment based on entropy and cloud model theory. Parameters of the Cloud Model of each eutrophication level were calculated with the chosen indicators； and hybrid entropy weight swere determined based on Shannon entropy and AHP to generate an Entropy-Cloud Model of all indicators. Certainty degrees of each level were calculated by the Entropy-Cloud Model； and the fuzzy entropy of certainty degrees was calculated to indicate the complexity of eutrophication. Eutrophication of 12 lakes and reservoirs were assessed by the Entropy-Cloud Model. Comparative studies with variable fuzzy sets，artificial neural network and normal cloud model show that the Entropy-Cloud Model is effective and intuitive， which can assess the eutrophication from two aspects of level and complexity. Different from other methods，this model provides a new way of eutrophication assessment. |
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