Environmental disasters such as flash floods are becoming more and more prevalent and carry an increasing burden to human civilization. They are usually unpredictable, fast in development and extend across large geographical areas. The consequences of such disasters can be reduced through better monitoring, for example using mobile sensing platforms that can give timely and accurate information to first responders and the public. Given the extended scale of the areas to monitor, and the time-varying nature of the phenomenon, we need fast algorithms to quickly determine the best sequence of locations to be monitored. This problem is very challenging: the present informative mobile sensor routing algorithms are either short-sighted or computationally demanding when applied to large scale systems. In this paper, a real-time sensor task scheduling algorithm that suits the features and needs of city-scale environmental monitoring tasks is proposed. The algorithm is run in forward search and makes use of the predictions of an associated distributed parameter system, modeling flash flood propagation. It partly inherits the causal relation expressed by a search tree, which describes all possible sequential decisions. The computationally heavy data assimilation steps in the forward search tree are replaced by functions dependent on the covariance matrix between observation sets. Taking flood tracking in an urban area as a concrete example, numerical experiments in this paper indicate that this scheduling algorithm can achieve better results than myopic planning algorithms and other heuristics based sensor placement algorithms. Furthermore, this paper relies on a deep learning-based data-driven model to track the system states, and experiments suggest that popular estimation techniques have very good performance when applied to precise data-driven models. The data and code can be freely downloaded from https://drive.google.com/drive/folders/1gRz4T2KGFXtlnSugarfUL8r355cXb7Ko?usp=sharing.

诸如洪水之类的环境灾难正变得越来越普遍，给人类文明带来了越来越大的负担。它们通常是不可预测的，发展很快，并且扩展到较大的地理区域。可以通过更好的监控来减少此类灾难的后果，例如，使用可以向急救人员和公众及时提供准确信息的移动感应平台。考虑到要监视的区域的扩展范围以及该现象的时变性质，我们需要快速的算法来快速确定要监视的最佳位置顺序。这个问题非常具有挑战性：当应用于大规模系统时，目前的信息量丰富的移动传感器路由算法要么是短视的，要么是计算上的需求。在本文中，提出了一种适合城市规模环境监测任务特征和需求的实时传感器任务调度算法。该算法在正向搜索中运行，并利用关联的分布式参数系统的预测来对洪水泛洪传播进行建模。它部分继承了由搜索树表示的因果关系，该树描述了所有可能的顺序决策。前向搜索树中计算量大的数据同化步骤由依赖于观察集之间的协方差矩阵的函数取代。以市区的洪水跟踪为例，数值实验表明，该调度算法比近视规划算法和其他基于启发式的传感器放置算法具有更好的效果。此外，本文依靠基于深度学习的数据驱动模型来跟踪系统状态，实验表明，当将流行的估计技术应用于精确的数据驱动模型时，其性能非常好。可以从https://drive.google.com/drive/folders/1gRz4T2KGFXtlnSugarfUL8r355cXb7Ko?usp=sharing免费下载数据和代码。