Work

Physics‐Informed Fusion Learning for Nowcasting Fault Detection of Hydrological Multi Sensor Network

Year: 2026

Type: article

Abstract: Urban flood monitoring demands timely, reliable decisions under urgent and often unseen conditions. However, faults in sensor networks can undermine response, leading to human and economic losses. To address these issues, we propose a Nowcasting Physics‐Informed (NPI) model for fault detection in streamflow sensors, with a 100‐min memory window at 2‐min resolution. A Long Short‐Term Memory (LSTM) consumes fused features from measured signals and Storm Water Management Model simulator outputs to produce end‐of‐window fault probabilities. To ensure operational reliability, a lightweight per‐sensor eXtreme Gradient Boosting (XGBoost) stage calibrates the LSTM score using window‐local statistics of the measurement. In this study, we investigate whether and to what extent fusing a physical model with sensor data improves fault detection, and whether per‐sensor threshold calibration matters. Our results indicated yes. The Area under the precision–recall curve (AUC‐PR) was improved by 3.7%–12.5% over the physics‐informed model compared to baseline non‐physics‐informed learning. In addition, post‐hoc calibration improved AUC‐PR by an additional 0.2%–2.3%. Across five cross‐validation folds, F1‐score (F1) increased from 0.75 for the non‐physics LSTM baseline to 0.83 with calibration, and further to 0.88 for the physics informed model. Adding post‐hoc calibration to the physics informed model yielded the best result at 0.90. The NPI is causal and auditable, exporting calibrated probabilities and binary alerts for direct operator use without retraining the LSTM, and offers a practical template for warning systems and training strategies that remain robust to unseen conditions.

Source: Water Resources Research

DOI: 10.1029/2026wr043436

Authors: Farid Mousavi, Ali Torabi Haghighi, Jari Silander, Mehdi Monemi, Mehdi Rasti

Institutions: University of Oulu, Finnish Environment Institute

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,000