Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands.
Sensors (Basel). 2012 Nov 26;12(12):16274-90. doi: 10.3390/s121216274.
In case of an environmental accident, initially available data are often insufficient for properly managing the situation. In this paper, new sensor observations are iteratively added to an initial sample by maximising the global expected value of information of the points for decision making. This is equivalent to minimizing the aggregated expected misclassification costs over the study area. The method considers measurement error and different costs for class omissions and false class commissions. Constraints imposed by a mobile sensor web are accounted for using cost distances to decide which sensor should move to the next sample location. The method is demonstrated using synthetic examples of static and dynamic phenomena. This allowed computation of the true misclassification costs and comparison with other sampling approaches. The probability of local contamination levels being above a given critical threshold were computed by indicator kriging. In the case of multiple sensors being relocated simultaneously, a genetic algorithm was used to find sets of suitable new measurement locations. Otherwise, all grid nodes were searched exhaustively, which is computationally demanding. In terms of true misclassification costs, the method outperformed random sampling and sampling based on minimisation of the kriging variance.
在环境事故的情况下，最初获得的数据通常不足以妥善处理这种情况。在本文中，新的传感器观测值通过最大化用于决策的点的全局信息期望价值被迭代地添加到初始样本中。这相当于在研究区域内最小化总预期分类错误成本。该方法考虑了测量误差以及分类遗漏和错误分类的不同成本。通过使用成本距离来决定哪个传感器应该移动到下一个样本位置，考虑到了移动传感器网络施加的约束。该方法使用静态和动态现象的合成示例进行了演示。这允许计算真实的分类错误成本，并与其他采样方法进行比较。通过指示克里金法计算局部污染水平超过给定临界阈值的概率。在同时重新定位多个传感器的情况下，使用遗传算法找到一组合适的新测量位置。否则，将对所有网格节点进行详尽搜索，这在计算上是很费力的。就真实的分类错误成本而言，该方法优于随机采样和基于克里金方差最小化的采样。
