Matching Urban Flood Sensor Placement to Monitoring Objectives Using Bayesian Optimal Experimental Design

2026-08-21

AI Summary

该研究通过贝叶斯最优实验设计方法，针对城市洪水监测传感器的放置位置进行了优化，以匹配不同的监测目标，并使用仿真模型和神经网络代理来评估不同目标下的信息增益。

Abstract

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.

Problem

• Flood-monitoring sensors
• Optimal experimental design
• Expected information gain
• Sensor placement
• Monitoring objectives

Innovation

• Bayesian Optimal Experimental Design
• tRIBS-Urban Simulations
• Neural-Network Surrogate
• Goal-Oriented OED
• Parameter-Oriented OED