AI 中文总结
该研究以2014年底特律洪水为对象，用tRIBS-Urban模拟与神经网络代理模型，对比两类最优实验设计，发现监测目标会影响城市洪水传感器布设，提出以目标为先的布设工作流程。
AI 中文摘要
洪水监测传感器通常根据覆盖范围、可达性或预期淹没情况进行布设，但测量的价值取决于其旨在支持的预测或决策。本研究利用tRIBS-Urban模型模拟及2014年8月底特律大都会区洪水的神经网络代理模型，探究学习目标如何改变单传感器布设方案。在2576个候选位置中，我们对比了面向参数的最优实验设计（PO-OED，其价值在于模型参数的预期信息增益（EIG））与面向目标的最优实验设计（GO-OED，其价值在于指定洪水预测的EIG）；同时分析了参数EIG在事件过程中的演化，表明参数学习在不同位置和预见期转化为预测不确定性降低的程度并不均匀。在GO-OED下，点深度目标倾向于选择附近位置，而区域平均及区域最大深度目标则可能倾向于非本地位置；加权多点目标则保留了类似的广泛空间模式，但其计算得到的最大EIG位置有所不同。公共地理空间数据还为布设筛选提供了可行性和背景分类的示例。这些结果表明，在最优实验设计中监测目标会影响传感器布设，推动形成以目标为先的工作流程：明确预期预测与优先级、应用经现场验证的限制条件、按EIG对位置进行排序。
英文摘要
Flood-monitoring sensors are often placed according to coverage, access, or expected inundation. However, the value of a measurement depends on the prediction or decision it is intended to inform. Using tRIBS-Urban simulations and a neural-network surrogate of the August 2014 metropolitan Detroit flood, we examine how this learning target changes single-sensor placement. Across 2,576 candidate locations, we compare parameter-oriented optimal experimental design (PO-OED), which values expected information gain (EIG) about model parameters, with goal-oriented optimal experimental design (GO-OED), which values EIG about specified flood predictions. We also examine how parameter EIG evolves during the event, and illustrate that parameter learning translates unevenly into reductions in predictive uncertainty across locations and lead times. Under GO-OED, point-depth targets favor nearby locations, whereas regional-average and regional maximum-depth targets can favor nonlocal locations. Weighted multi-point objectives retain similar broad spatial patterns, although their computed max-EIG locations differ. Public geospatial data further provide illustrative feasibility and contextual classifications for deployment screening. These results show how monitoring objectives shape sensor placement in optimal experimental design, and motivates an objective-first workflow that defines the intended prediction and priorities, applies field-verified restrictions, and ranks locations by EIG.
