The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation

• URL: http://arxiv.org/abs/2310.19166v1
• Date: Sun, 29 Oct 2023 21:56:22 GMT
• Title: The Power of Explainability in Forecast-Informed Deep Learning Models for Flood Mitigation
• Authors: Jimeng Shi, Vitalii Stebliankin, Giri Narasimhan
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Floods can cause horrific harm to life and property. However, they can be mitigated or even avoided by the effective use of hydraulic structures such as dams, gates, and pumps. By pre-releasing water via these structures in advance of extreme weather events, water levels are sufficiently lowered to prevent floods. In this work, we propose FIDLAR, a Forecast Informed Deep Learning Architecture, achieving flood management in watersheds with hydraulic structures in an optimal manner by balancing out flood mitigation and unnecessary wastage of water via pre-releases. We perform experiments with FIDLAR using data from the South Florida Water Management District, which manages a coastal area that is highly prone to frequent storms and floods. Results show that FIDLAR performs better than the current state-of-the-art with several orders of magnitude speedup and with provably better pre-release schedules. The dramatic speedups make it possible for FIDLAR to be used for real-time flood management. 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.

Related papers

• 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)
• Automated Floodwater Depth Estimation Using Large Multimodal Model for Rapid Flood Mapping [0.0]
  This paper presents an automated and fast approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used specifically for estimating floodwater. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos.
  arXiv  Detail & Related papers  (2024-02-26T16:02:15Z)
• 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)
• Graph Transformer Network for Flood Forecasting with Heterogeneous Covariates [2.694662116529994]
  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.
  arXiv  Detail & Related papers  (2023-10-11T16:24:06Z)
• MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features [0.8497188292342053]
  This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County. MaxFloodCast offers efficient and interpretable flood inundation depth predictions.
  arXiv  Detail & Related papers  (2023-08-11T16:58:57Z)
• 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)
• Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting [52.77986479871782]
  Global warming made the Arctic available for marine operations and created demand for reliable operational sea ice forecasts. In this work, we investigate the performance of the U-Net model trained in two regimes for predicting sea ice for up to the next 10 days. We show that this deep learning model can outperform simple baselines by a significant margin and improve its quality by using additional weather data and training on multiple regions.
  arXiv  Detail & Related papers  (2022-10-17T09:14:35Z)
• Learning-based estimation of in-situ wind speed from underwater acoustics [58.293528982012255]
  We introduce a deep learning approach for the retrieval of wind speed time series from underwater acoustics. Our approach bridges data assimilation and learning-based frameworks to benefit both from prior physical knowledge and computational efficiency.
  arXiv  Detail & Related papers  (2022-08-18T15:27:40Z)
• Determination of building flood risk maps from LiDAR mobile mapping data [11.128919681798664]
  Flood simulations can provide early warnings for areas and buildings at risk of flooding. Knowing the heights of facade openings helps to identify places that are more susceptible to water ingress. This research presents a new process for the extraction of windows and doors from LiDAR mobile mapping data.
  arXiv  Detail & Related papers  (2022-01-14T15:36:08Z)
• 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)

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.