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Multiobjective Optimization of Sensor Placement for Precipitation Station Monitoring Network Design

机译:沉淀站监控网络设计传感器放置的多目标优化

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摘要

An optimal sensor placement of a precipitation station network should fulfill different regulations and requirements, such as coverage maximization, easy access, and uniform distribution. However, few studies have focused on an integrated way to optimize the precipitation network design from the perspective of monitoring efficiency in space. In this paper, given the complex requirements and diversified goals for precipitation monitoring, a new multiobjective location model is established for optimizing the network's monitoring efficiency with a comprehensive weighting scheme. Based on the precipitation station siting regulations, the spatial coverage, accessibility, and dispersion of stations are considered in the model. The Elitist Nondominated Sorting Genetic Algorithm (NSGA-Ⅱ) is used to obtain a set of Pareto-efficient solutions. The Jinsha River Basin is selected as the study region to test the proposed method. The results show that the proposed method satisfies the complex precipitation monitoring requirements and achieves higher coverage than the real-world deployment. The decision making for siting schemes, comparison of other dispersion models, and the extensibility of the proposed method are also discussed.
机译:降水站网络的最佳传感器放置应满足不同的规定和要求,例如覆盖范围,易于访问和均匀分布。然而,很少有研究专注于综合方式,以从空间监测效率的角度来优化降水网络设计。在本文中,鉴于降水监测的复杂要求和多元化的目标,建立了一种新的多目标位置模型,以优化网络的监测效率,通过全面的加权方案。基于沉淀站选址规定,在模型中考虑了站点的空间覆盖,可访问性和分散。 Elitist NondoMinated分类遗传算法(NSGA-Ⅱ)用于获得一组静态效率的解决方案。锦沙河流域被选为研究所提出的方法。结果表明,该方法满足了复杂的降水监测要求,实现了比现实世界部署更高的覆盖范围。还讨论了用于选址方案的决策,其他分散模型的比较以及所提出的方法的可扩展性。

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