MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features

• URL: http://arxiv.org/abs/2308.06228v1
• Date: Fri, 11 Aug 2023 16:58:57 GMT
• Title: MaxFloodCast: Ensemble Machine Learning Model for Predicting Peak Inundation Depth And Decoding Influencing Features
• Authors: Cheng-Chun Lee, Lipai Huang, Federico Antolini, Matthew Garcia, Andrew Juanb, Samuel D. Brody, Ali Mostafavi
• Abstract summary: 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.
• Score: 0.8497188292342053
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R-squared of 0.949 and a Root Mean Square Error of 0.61 ft on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

Related papers

• FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation [2.8532862791847053]
  FloodDamageCast is a machine learning framework tailored for property flood damage nowcasting. The framework leverages heterogeneous data to predict residential flood damage at a resolution of 500 meters by 500 meters within Harris County, Texas, during the 2017 Hurricane Harvey. Insights gleaned from flood damage nowcasting can assist emergency responders to more efficiently identify repair needs, allocate resources, and streamline on-the-ground inspections.
  arXiv  Detail & Related papers  (2024-05-23T07:04:41Z)
• 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)
• CasCast: Skillful High-resolution Precipitation Nowcasting via Cascaded Modelling [93.65319031345197]
  We propose CasCast, a cascaded framework composed of a deterministic and a probabilistic part to decouple predictions for mesoscale precipitation distributions and small-scale patterns. CasCast significantly surpasses the baseline (up to +91.8%) for regional extreme-precipitation nowcasting.
  arXiv  Detail & Related papers  (2024-02-06T08:30:47Z)
• 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)
• 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)
• Predicting Road Flooding Risk with Machine Learning Approaches Using Crowdsourced Reports and Fine-grained Traffic Data [1.0554048699217669]
  The objective of this study is to predict road flooding risks based on topographic, hydrologic, and temporal precipitation features using machine learning models. The findings from Hurricane Harvey indicate that precipitation is the most important feature for predicting road inundation susceptibility. This study advances the emerging field of smart flood resilience in terms of predictive flood risk mapping at the road level.
  arXiv  Detail & Related papers  (2021-08-30T14:25:58Z)
• 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)
• Predictive Analytics for Water Asset Management: Machine Learning and Survival Analysis [55.41644538483948]
  We study a statistical and machine learning framework for the prediction of water pipe failures. We use a dataset containing the failure records of all pipes within the water distribution network in Barcelona, Spain. The results shed light on the effect of important risk factors, such as pipe geometry, age, material, and soil cover, among others.
  arXiv  Detail & Related papers  (2020-07-02T19:08:36Z)

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.