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

2025-10-15

AI Summary

To address decision latency and perceptual limitations in flood emergency response caused by budgetary constraints and data scarcity, this paper proposes a decision-oriented end-to-end joint optimization framework. Unlike conventional static sensor placement and task-agnostic flood forecasting, our approach unifies sensor configuration, spatiotemporal flood prediction, and emergency decision-making within a single differentiable model. We introduce Implicit Maximum Likelihood Estimation (I-MLE) to enable gradient-based optimization over discrete sensor selections, and integrate a context-scoring network, a differentiable sensor selection module, a spatiotemporal reconstruction-based predictive model, and a probabilistic differentiable decision head. Under budget constraints, the framework significantly reduces decision regret, improves decision quality at equivalent sensing gains and prediction errors, and enhances system robustness and task adaptability.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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. To address this, we introduce a novel decision-focused framework that strategically selects locations for in-situ sensor placement and optimize spatio-temporal flood forecasting models to optimize downstream flood response decision regrets. Our end-to-end pipeline integrates four components: a contextual scoring network, a differentiable sensor selection module under hard budget constraints, a spatio-temporal flood reconstruction and forecasting model, and a differentiable decision layer tailored to task-specific objectives. Central to our approach is the incorporation of Implicit Maximum Likelihood Estimation (I-MLE) to enable gradient-based learning over discrete sensor configurations, and probabilistic decision heads to enable differentiable approximation to various constrained disaster response tasks.

Problem

• Optimizing sensor placement and flood forecasting for emergency response decisions
• Addressing limitations of traditional task-agnostic flood management strategies
• Developing differentiable framework to minimize flood response decision regrets

Innovation

• Decision-focused framework optimizes sensor placement and forecasting
• Implicit Maximum Likelihood Estimation enables gradient-based discrete learning
• Differentiable decision layer approximates disaster response task objectives