PIFF: A Physics-Informed Generative Flow Model for Real-Time Flood Depth Mapping

• URL: http://arxiv.org/abs/2511.09130v1
• Date: Thu, 13 Nov 2025 01:34:18 GMT
• Title: PIFF: A Physics-Informed Generative Flow Model for Real-Time Flood Depth Mapping
• Authors: ChunLiang Wu, Tsunhua Yang, Hungying Chen,
• Abstract summary: We propose PIFF, a physics-informed, flow-based generative neural network for near real-time flood depth estimation.<n>Built on an image-to-image generative framework, it efficiently maps Digital Elevation Models (DEM) to flood depth predictions.<n>Using a 26 km study area in Tainan, Taiwan, with 182 rainfall scenarios, our results demonstrate that PIFF offers an effective, data-driven alternative for flood prediction and response.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flood mapping is crucial for assessing and mitigating flood impacts, yet traditional methods like numerical modeling and aerial photography face limitations in efficiency and reliability. To address these challenges, we propose PIFF, a physics-informed, flow-based generative neural network for near real-time flood depth estimation. Built on an image-to-image generative framework, it efficiently maps Digital Elevation Models (DEM) to flood depth predictions. The model is conditioned on a simplified inundation model (SPM) that embeds hydrodynamic priors into the training process. Additionally, a transformer-based rainfall encoder captures temporal dependencies in precipitation. Integrating physics-informed constraints with data-driven learning, PIFF captures the causal relationships between rainfall, topography, SPM, and flooding, replacing costly simulations with accurate, real-time flood maps. Using a 26 km study area in Tainan, Taiwan, with 182 rainfall scenarios ranging from 24 mm to 720 mm over 24 hours, our results demonstrate that PIFF offers an effective, data-driven alternative for flood prediction and response.

Related papers

• AI Driven Water Segmentation with deep learning models for Enhanced Flood Monitoring [0.0]
  Flooding is a major natural hazard causing significant fatalities and economic losses annually, with increasing frequency due to climate change.<n>This study compares the performance of three deep learning models UNet, ResNet, and DeepLabv3 for pixelwise water segmentation to aid in flood detection, utilizing images from drones, in field observations, and social media.
  arXiv  Detail & Related papers  (2025-01-14T17:26:02Z)
• Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
  Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models. Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML) We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
  arXiv  Detail & Related papers  (2024-07-18T15:44:23Z)
• Deep Neural Networks with 3D Point Clouds for Empirical Friction Measurements in Hydrodynamic Flood Models [0.0]
  Friction factors (FFs) are used to calculate momentum losses in flood models. Flood models often rely on surrogate observations (such as land use) to estimate FFs, introducing uncertainty. This research presents a laboratory-trained Deep Neural Network (DNN) trained using flume experiments with data augmentation techniques to measure Manning's n based on Point Cloud data.
  arXiv  Detail & Related papers  (2024-04-02T18:44:53Z)
• Rapid Flood Inundation Forecast Using Fourier Neural Operator [77.30160833875513]
  Flood inundation forecast provides critical information for emergency planning before and during flood events. High-resolution hydrodynamic modeling has become more accessible in recent years, however, predicting flood extents at the street and building levels in real-time is still computationally demanding. We present a hybrid process-based and data-driven machine learning (ML) approach for flood extent and inundation depth prediction.
  arXiv  Detail & Related papers  (2023-07-29T22:49:50Z)
• Deep Learning Models for Flood Predictions in South Florida [0.0]
  Simulating and predicting the water level/stage in river systems is essential for flood warnings, hydraulic operations, and flood mitigations.<n>We train several deep learning (DL) models for use as surrogate models to rapidly predict the water stage.<n>A portion of the Miami River in South Florida was chosen as a case study for this paper.
  arXiv  Detail & Related papers  (2023-06-28T04:15:01Z)
• An evaluation of deep learning models for predicting water depth evolution in urban floods [59.31940764426359]
  We compare different deep learning models for prediction of water depth at high spatial resolution. Deep learning models are trained to reproduce the data simulated by the CADDIES cellular-automata flood model. Our results show that the deep learning models present in general lower errors compared to the other methods.
  arXiv  Detail & Related papers  (2023-02-20T16:08:54Z)
• Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
  Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars. They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
  arXiv  Detail & Related papers  (2021-07-14T21:10:47Z)
• Semi-Supervised Video Deraining with Dynamic Rain Generator [59.71640025072209]
  This paper proposes a new semi-supervised video deraining method, in which a dynamic rain generator is employed to fit the rain layer. Specifically, such dynamic generator consists of one emission model and one transition model to simultaneously encode the spatially physical structure and temporally continuous changes of rain streaks. Various prior formats are designed for the labeled synthetic and unlabeled real data, so as to fully exploit the common knowledge underlying them.
  arXiv  Detail & Related papers  (2021-03-14T14:28:57Z)
• From Rain Generation to Rain Removal [67.71728610434698]
  We build a full Bayesian generative model for rainy image where the rain layer is parameterized as a generator. We employ the variational inference framework to approximate the expected statistical distribution of rainy image. Comprehensive experiments substantiate that the proposed model can faithfully extract the complex rain distribution.
  arXiv  Detail & Related papers  (2020-08-08T18:56:51Z)
• Breaking the Limits of Remote Sensing by Simulation and Deep Learning for Flood and Debris Flow Mapping [13.167695669500391]
  We propose a framework that estimates inundation depth and debris-flow-induced topographic deformation from remote sensing imagery. A water and debris flow simulator generates training data for various artificial disaster scenarios. We show that regression models based on Attention U-Net and LinkNet architectures trained on such synthetic data can predict the maximum water level and topographic deformation.
  arXiv  Detail & Related papers  (2020-06-09T10:59:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.