Work

Decision-focused Sensing and Forecasting for Adaptive and Rapid Flood Response: An Implicit Learning Approach

Year: 2025

Type: conference-paper

Abstract: Timely and reliable decision-making is vital for flood emergency response, yet it remains severely hindered by limited and imprecise situational awareness due to various budget and data accessibility constraints. Traditional flood management systems often rely on in-situ sensors to calibrate remote sensing-based large-scale flood depth forecasting models, and further take flood depth estimates to optimize flood response decisions. However, these approaches often take fixed, decision task-agnostic strategies to decide where to put in-situ sensors (e.g., maximize overall information gain) and train flood forecasting models (e.g., minimize average forecasting errors), but overlook that systems with the same sensing gain and average forecasting errors may lead to distinct decisions.

Source: ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)

DOI: 10.1145/3736425.3770102

Authors: Qian Sun, Graham Hults, Susu Xu

Institutions: Johns Hopkins University, Hong Kong University of Science and Technology

Topic: Flood Risk Assessment and Management

Subfield: Global and Planetary Change

Field: Environmental Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Funder: National Science Foundation

Awards: CAREER: Open-source Probabilistic AI Infrastructure for Dynamic Cascading Disaster Impacts Modeling and Assessment, Rapid, Scalable, and Joint Assessment of Seismic Multi-Hazards and Impacts: From Satellite Images to Causality-Informed Deep Bayesian Networks