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首页> 外文会议>IFIP(International Federation for Information Processing) TC 12 International Conference on Computer and Computing Technologies in Agriculture(CCTA 2007); 20070818-20; Wuyishan(CN) >OPTIMIZED SELECTION OF WETLAND WATER QUALITY MONITORING POINTS BASEDON INFORMATION ENTROPY AND FUZZYSIMILARITY
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OPTIMIZED SELECTION OF WETLAND WATER QUALITY MONITORING POINTS BASEDON INFORMATION ENTROPY AND FUZZYSIMILARITY

机译：基于信息熵和模糊相似度的湿地水质监测点优化选择

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摘要

Known as the kidney of earth, wetland has significant ecological functions such as freshwater conservation, poison elimination, carbon storage, water quality purification, flood storage and drought control, climate regulation and remaining biodiversity etc. So protecting wetland is protecting ourselves. Water environment quality best reflects the ecological environment condition of wetland. According to multi-index and Spatial and Temporal variation of wetland water pollution, combining optimized selection requirements of wetland water quality monitoring, fuzzy similarity is propose. Through constructing multi-index monitoring data samples Decision-making Matrix, fuzzy similarity matrix between sample data and their mean values is established. According to the index value variation, the index weights are calculated based on information entropy theory. With the index weight and sample fuzzy similarity matrix, comprehensive fuzzy similarity of each monitoring point is calculated. Finally, according to comprehensive fuzzy similarity, each monitoring point is reasonably clustered, then representative points is selected from each category, so distribution optimization could be realized. Practical running proves that this scheme is simple and feasible, and extensionally applied to optimize other environmental monitoring points.
机译：湿地被称为地球的肾脏，具有重要的生态功能，例如淡水保护，除毒，碳存储，水质净化，洪水存储和干旱控制，气候调节以及剩余生物多样性等。因此保护湿地就是保护自己。水环境质量最能反映湿地的生态环境状况。结合湿地水质污染的多指标和时空变化，结合湿地水质监测的优化选择要求，提出模糊相似度。通过构建多指标监测数据样本决策矩阵，建立了样本数据与其均值之间的模糊相似度矩阵。根据指标值的变化，基于信息熵理论计算指标权重。利用指标权重和样本模糊相似度矩阵，计算出每个监测点的综合模糊相似度。最后，根据综合模糊相似度，合理地对每个监测点进行聚类，然后从每个类别中选择代表点，从而实现分配优化。实际运行证明，该方案简单可行，可广泛应用于优化其他环境监测点。

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