Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations

• URL: http://arxiv.org/abs/2406.15451v1
• Date: Thu, 6 Jun 2024 19:54:34 GMT
• Title: Deep Vision-Based Framework for Coastal Flood Prediction Under Climate Change Impacts and Shoreline Adaptations
• Authors: Areg Karapetyan, Aaron Chung Hin Chow, Samer Madanat,
• Abstract summary: We present a systematic framework for training high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. We also introduce a deep CNN architecture tailored specifically to the coastal flood prediction problem at hand. The performance of the developed DL models is validated against commonly adopted geostatistical regression methods.
• Score: 0.3413711585591077
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In light of growing threats posed by climate change in general and sea level rise (SLR) in particular, the necessity for computationally efficient means to estimate and analyze potential coastal flood hazards has become increasingly pressing. Data-driven supervised learning methods serve as promising candidates that can dramatically expedite the process, thereby eliminating the computational bottleneck associated with traditional physics-based hydrodynamic simulators. Yet, the development of accurate and reliable coastal flood prediction models, especially those based on Deep Learning (DL) techniques, has been plagued with two major issues: (1) the scarcity of training data and (2) the high-dimensional output required for detailed inundation mapping. To remove this barrier, we present a systematic framework for training high-fidelity Deep Vision-based coastal flood prediction models in low-data settings. We test the proposed workflow on different existing vision models, including a fully transformer-based architecture and a Convolutional Neural Network (CNN) with additive attention gates. Additionally, we introduce a deep CNN architecture tailored specifically to the coastal flood prediction problem at hand. The model was designed with a particular focus on its compactness so as to cater to resource-constrained scenarios and accessibility aspects. The performance of the developed DL models is validated against commonly adopted geostatistical regression methods and traditional Machine Learning (ML) approaches, demonstrating substantial improvement in prediction quality. Lastly, we round up the contributions by providing a meticulously curated dataset of synthetic flood inundation maps of Abu Dhabi's coast produced with a physics-based hydrodynamic simulator, which can serve as a benchmark for evaluating future coastal flood prediction models.

Related papers

• Evaluation of deep learning models for Australian climate extremes: prediction of streamflow and floods [0.17999333451993949]
  In recent years, climate extremes such as floods have created significant environmental and economic hazards for Australia. Deep learning methods have been promising for predicting small to medium-sized climate extreme events over a short time horizon. We present an ensemble-based machine learning approach that addresses large-scale extreme flooding challenges.
  arXiv  Detail & Related papers  (2024-07-20T23:45:04Z)
• 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)
• Outlier detection in maritime environments using AIS data and deep recurrent architectures [5.399126243770847]
  We present a methodology based on deep recurrent models for maritime surveillance, over publicly available Automatic Identification System (AIS) data. The setup employs a deep Recurrent Neural Network (RNN)-based model, for encoding and reconstructing the observed ships' motion patterns. Our approach is based on a thresholding mechanism, over the calculated errors between observed and reconstructed motion patterns.
  arXiv  Detail & Related papers  (2024-06-14T12:15:15Z)
• Graph neural network-based surrogate modelling for real-time hydraulic prediction of urban drainage networks [1.8073031015436376]
  Physics-based models are computationally time-consuming and infeasible for real-time scenarios of urban drainage networks. Fully-connected neural networks (NNs) are potential surrogate models, but may suffer from low interpretability and efficiency in fitting complex targets. This work proposes a GNN-based surrogate of the flow routing model for the hydraulic prediction problem of drainage networks.
  arXiv  Detail & Related papers  (2024-04-16T07:08:04Z)
• Large-scale flood modeling and forecasting with FloodCast [22.09906304112966]
  We build a fast, stable, accurate, resolution-invariant, and geometry-adaptative flood modeling and forecasting framework. The framework comprises two main modules: multi-satellite observation and hydrodynamic modeling. In the hydrodynamic modeling module, a geometry-adaptive physics-informed neural solver (GeoPINS) is introduced. Building upon GeoPINS, we propose a sequence-to-sequence GeoPINS model to handle long-term temporal series and extensive spatial domains in large-scale flood modeling.
  arXiv  Detail & Related papers  (2024-03-18T20:18:32Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures [31.879514593973195]
  We propose a flow and depth forecasting model, trained to jointly forecast both modalities at once. We train the proposed model to also perform predictions for several timesteps in the future. We report benefits on the downstream task of segmentation forecasting, injecting our predictions in a flow-based mask-warping framework.
  arXiv  Detail & Related papers  (2023-10-31T16:30:16Z)
• Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling [58.456404022536425]
  State of the art for physical hazard prediction from weather and climate requires expensive km-scale numerical simulations driven by coarser resolution global inputs. Here, a generative diffusion architecture is explored for downscaling such global inputs to km-scale, as a cost-effective machine learning alternative. The model is trained to predict 2km data from a regional weather model over Taiwan, conditioned on a 25km global reanalysis.
  arXiv  Detail & Related papers  (2023-09-24T19:57:22Z)
• 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)
• Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking [76.27433308688592]
  Accurately modeling quadrotor's system dynamics is critical for guaranteeing agile, safe, and stable navigation. We present a novel Physics-Inspired Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system dynamics purely from robot experience. Our approach combines the expressive power of sparse temporal convolutions and dense feed-forward connections to make accurate system predictions.
  arXiv  Detail & Related papers  (2022-06-07T13:51:35Z)

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.