Spatial Analysis on the Layout of Groundwater Quality Monitoring Network
An efficient monitoring network not only provides the dynamic and synchronous information and reliable sources for scientific research in the groundwater environment, but helps policy-makers in efficient decisions, so a reasonable layout of groundwater quality monitoring network is of great importance. With regard to the spatial analysis of it, a complete set of ideas is put forward: firstly, evaluate the pollution condition of the monitoring area by principal component analysis (PCA); secondly, the monitoring area is stratified based on the historical data and expert experience requiring similar characteristics within each layer and different characteristics between layers, the result of which makes the monitoring indicators and monitoring density be different for different environmental features, note here, the areas where the monitoring wells are not convenient or needed to install can be stratified as a spatial layer, which can not to be taken into account in the gridding afterward; thirdly, the main monitoring indicators for monitoring area (or each layer) are determined by PCA; fourthly, analyze the spatial layout of the groundwater quality monitoring network, step1: compute the mutual information of the monitoring area(or each layer) except the special layer; step2: gridding the monitoring area(or each layer) except the special layer, the size of which is obtained based on the mutual information for each indicator, when multiple variables are monitored in a well simultaneously, the unified grid size needs to be fixed by the following two methods: 1)using PCA to determine, 2)using the minimum or mean of the grid sizes to be as the unified grid size, step3: compare the layouts of the monitoring well designed through the single indicator and multiple indicators; fifthly, the final layout of the groundwater quality monitoring network is presented according to the stratified information, existing monitoring wells and actual hydrogeological conditions. The idea and results can be as a reference for the future optimization.
spatial analysis:information entropy:principal component analysis (PCA):stratification
Yansha Guo Jinfeng Wang
State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Beijing,China
国际会议
The 18th International Conference on Geoinformatics(第18届国际地理信息科学与技术大会 Geoinformatics 2010)
北京
英文
1433-1438
2010-06-18（万方平台首次上网日期，不代表论文的发表时间）