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Development of a selection system of the optimal sampling locations for a soil pollution survey.

开发土壤污染调查最佳采样点选择系统。

基本信息

批准号：
12558072
负责人：
YONEDA Minoru
金额：
$ 2.37万
依托单位：
KYOTO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2000
资助国家：
日本
起止时间：
2000 至 2003
项目状态：
已结题

项目摘要

A new method for optimal strategy of sampling locations for soil pollution was developed using geostatistics. The evaluation function was created as a estimate, of the differences between geostatistically. estimated fields and fictitious real fields created by the unconditional simulation, which was a geostatistical Monte Carlo method. The difference was evaluated by sum of squares of the difference of concentration at each location in a whole field or a square of the difference of the sum of a pollution material in a whole field. The method could also treat with uncertain information by selecting fields which could meet the given information from the fictitious real fields created by the unconditional simulation. Improvement of genetic algorithms and development of a new method named "stepwise movement to the best point (SMBP)" which could always reach a local solution were executed. Their hybrid method was shown to be effective for searching the optimal solution. Characteristics 'and problems in the parameter estimation by most likelihood method was also studied. The results showed that the correlation scale estimated by the most likelihood method was apt of be smaller than the real one and that uncertainty at unknown points could not be necessarily estimated properly using the estimated parameters. The examples of soil pollution released by Japanese government showed that correlation scales estimated using the examples had log-normal distribution. Using these data as the experiential prior information, parameter estimation was thought to be improved.

应用地统计学原理，提出了一种土壤污染采样点优化的新方法。评价函数被创建为对地理统计学之间的差异的估计。估计的字段和虚构的真实的字段创建的无条件模拟，这是一个地质统计蒙特卡罗方法。通过整个场地中每个位置的浓度差的平方和或整个场地中污染材料的总和的差的平方来评估差异。该方法还可以通过从无条件模拟产生的虚拟真实的场中选择满足给定信息的场来处理不确定信息。对遗传算法进行了改进，并提出了一种新的算法“逐步移动到最佳点（SMBP）”，该算法总是能够达到局部解。结果表明，该混合算法能有效地搜索最优解.研究了最大似然法参数估计的特点和存在的问题。结果表明，用最大似然法估计的相关标度容易小于真实的相关标度，用估计的参数不一定能正确估计未知点的不确定度。日本政府公布的土壤污染实例表明，利用实例估算的相关尺度服从对数正态分布。利用这些数据作为经验先验信息，可以改进参数估计。