动态贴近度法及其在水质监测点优化布设中的应用

现有水质监测点优化布设方法存在两方面问题,一是只考虑水质随空间的变化,没有考虑水质随季节的变化;二是引入了相关水质标准作为参考点进行距离计算,得到的贴近度物理意义不够明确.为此,本文提出了新的水质监测点优化布设模型-动态贴近度法.该模型同时利用了水质监测数据在时间和空间两个维度上的信息,使优化布点的信息更加合理、全面;避免了不同水质标准值作为参考点带来的排序不一致问题.一个城市水质监测点优化布设的实例计算结果表明,利用贴近度优化模型的放大作用,使得聚类效果明显提高,易于进行水质监测点的分类和排序.

Dynamic similarity and its application to optimized layout in water quality monitoring of river

The present paper aims to introduce the authors' new model for water quality monitoring, i.e., the dynamic similarity method. It is well known that there exist two main problems with the current methods of optimal layout for water quality monitoring given by the various papers so far published at home. One is that only the changes of water quality in spatial space are considered, while the changes of water quality in time sphere are not taken into account at all. The other problem is that the physical meaning of the similarity achieved seems to be indistinctive when the value taken into the water quality standard as a reference point to calculating the distance to the assessment point. However, in our new model, changes of water quality monitoring information and data both in spatial and time sphere are all taken into full consideration. This makes the data used in the optimization process more sufficient and reasonable. In addition, the inconsistent arrangement of the assessment points caused by the use of the values of water quality standard as reference can be avoided. Thus, the results gained from a practical example indicate that the clustering effect of optimized layout for monitoring points has increased evidently due to the amplifying effect of similarity optimizing model.