Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates

• URL: http://arxiv.org/abs/2310.07631v1
• Date: Wed, 11 Oct 2023 16:24:06 GMT
• Title: Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates
• Authors: Jimeng Shi, Vitalii Stebliankin, Zhaonan Wang, Shaowen Wang, Giri Narasimhan
• Abstract summary: Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. FloodGTN learns the consequent-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) and an LSTM. We apply the FloodGTN tool to data from the South Florida Water Management District, which manages a coastal area prone to frequent storms and hurricanes.
• Score: 2.694662116529994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Floods can be very destructive causing heavy damage to life, property, and livelihoods. Global climate change and the consequent sea-level rise have increased the occurrence of extreme weather events, resulting in elevated and frequent flood risk. Therefore, accurate and timely flood forecasting in coastal river systems is critical to facilitate good flood management. However, the computational tools currently used are either slow or inaccurate. In this paper, we propose a Flood prediction tool using Graph Transformer Network (FloodGTN) for river systems. More specifically, FloodGTN learns the spatio-temporal dependencies of water levels at different monitoring stations using Graph Neural Networks (GNNs) and an LSTM. It is currently implemented to consider external covariates such as rainfall, tide, and the settings of hydraulic structures (e.g., outflows of dams, gates, pumps, etc.) along the river. We use a Transformer to learn the attention given to external covariates in computing water levels. We apply the FloodGTN tool to data from the South Florida Water Management District, which manages a coastal area prone to frequent storms and hurricanes. Experimental results show that FloodGTN outperforms the physics-based model (HEC-RAS) by achieving higher accuracy with 70% improvement while speeding up run times by at least 500x.

Related papers

• Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee [0.0]
  Ashland City, Tennessee, is highly susceptible to flooding due to increased upstream water levels. This study developed a robust flood prediction model for the city using water level data at 30-minute intervals from ten USGS gauge stations within the watershed. The model proved to be an effective tool for flood prediction in Ashland City, with potential applications for enhancing disaster preparedness and response efforts.
  arXiv  Detail & Related papers  (2024-05-16T18:14:59Z)
• TRG-Net: An Interpretable and Controllable Rain Generator [61.2760968459789]
  This study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, but also finely adapt to complicated and diverse practical rainy images. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks.
  arXiv  Detail & Related papers  (2024-03-15T03:27:39Z)
• FIDLAR: Forecast-Informed Deep Learning Architecture for Flood Mitigation [0.0]
  Floods can be mitigated or even prevented by strategically releasing water before extreme weather events with hydraulic structures such as dams, gates, pumps, and reservoirs. A standard approach used by local water management agencies is the "rule-based" method, which specifies predetermined pre-releases of water based on historical and time-tested human experience. We propose a Forecast Informed Deep Learning Architecture, FIDLAR, to achieve rapid and optimal flood management with precise water pre-releases.
  arXiv  Detail & Related papers  (2024-02-20T20:53:04Z)
• The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation [0.0]
  We propose FIDLAR, a Forecast Informed Deep Learning Architecture, to achieve flood management in watersheds with hydraulic structures. Results show FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup. The main contribution of this paper is the effective use of tools for model explainability, allowing us to understand the contribution of the various environmental factors towards its decisions.
  arXiv  Detail & Related papers  (2023-10-29T21:56:22Z)
• 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)
• 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)
• Flood Prediction Using Machine Learning Models [0.0]
  This paper aims to reduce the extreme risks of this natural disaster by providing a prediction for floods using different machine learning models. With the outcome, a comparative analysis will be conducted to understand which model delivers a better accuracy.
  arXiv  Detail & Related papers  (2022-08-02T03:59:43Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Physics-informed GANs for Coastal Flood Visualization [65.54626149826066]
  We create a deep learning pipeline that generates visual satellite images of current and future coastal flooding. By evaluating the imagery relative to physics-based flood maps, we find that our proposed framework outperforms baseline models in both physical-consistency and photorealism. While this work focused on the visualization of coastal floods, we envision the creation of a global visualization of how climate change will shape our earth.
  arXiv  Detail & Related papers  (2020-10-16T02:15:34Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

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.