FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction

• URL: http://arxiv.org/abs/2502.01550v1
• Date: Mon, 03 Feb 2025 17:30:45 GMT
• Title: FireCastNet: Earth-as-a-Graph for Seasonal Fire Prediction
• Authors: Dimitrios Michail, Charalampos Davalas, Lefki-Ioanna Panagiotou, Ioannis Prapas, Spyros Kondylatos, Nikolaos Ioannis Bountos, Ioannis Papoutsis,
• Abstract summary: We present FireCastNet, a novel architecture which combines a 3D convolutional encoder with GraphCast. FireCastNet is trained to capture the context leading to wildfires, at different spatial and temporal scales. Our investigation focuses on assessing the effectiveness of our model in predicting the presence of burned areas.
• Score: 2.748450182087935
• License:
• Abstract: With climate change expected to exacerbate fire weather conditions, the accurate and timely anticipation of wildfires becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forecasting with machine learning. For the predictive analysis, we present FireCastNet, a novel architecture which combines a 3D convolutional encoder with GraphCast, originally developed for global short-term weather forecasting using graph neural networks. FireCastNet is trained to capture the context leading to wildfires, at different spatial and temporal scales. Our investigation focuses on assessing the effectiveness of our model in predicting the presence of burned areas at varying forecasting time horizons globally, extending up to six months into the future, and on how different spatial or/and temporal context affects the performance. Our findings demonstrate the potential of deep learning models in seasonal fire forecasting; longer input time-series leads to more robust predictions, while integrating spatial information to capture wildfire spatio-temporal dynamics boosts performance. Finally, our results hint that in order to enhance performance at longer forecasting horizons, a larger receptive field spatially needs to be considered.

Related papers

• FengWu-W2S: A deep learning model for seamless weather-to-subseasonal forecast of global atmosphere [53.22497376154084]
  We propose FengWu-Weather to Subseasonal (FengWu-W2S), which builds on the FengWu global weather forecast model and incorporates an ocean-atmosphere-land coupling structure along with a diverse perturbation strategy. Our hindcast results demonstrate that FengWu-W2S reliably predicts atmospheric conditions out to 3-6 weeks ahead, enhancing predictive capabilities for global surface air temperature, precipitation, geopotential height and intraseasonal signals such as the Madden-Julian Oscillation (MJO) and North Atlantic Oscillation (NAO) Our ablation experiments on forecast error growth from daily to seasonal timescales reveal potential
  arXiv  Detail & Related papers  (2024-11-15T13:44:37Z)
• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• Seasonal Fire Prediction using Spatio-Temporal Deep Neural Networks [2.748450182087935]
  We utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forecasting with machine learning. For the predictive analysis, we train deep learning models with different architectures that capture wildfire-temporal context. Our findings demonstrate the great potential of deep learning models in seasonal fire forecasting.
  arXiv  Detail & Related papers  (2024-04-09T16:28:54Z)
• Explainable Global Wildfire Prediction Models using Graph Neural Networks [2.2389592950633705]
  We introduce an innovative Graph Neural Network (GNN)-based model for global wildfire prediction. Our approach transforms global climate and wildfire data into a graph representation, addressing challenges such as null oceanic data locations.
  arXiv  Detail & Related papers  (2024-02-11T10:44:41Z)
• GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
  We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data. It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute. We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
  arXiv  Detail & Related papers  (2022-12-24T18:15:39Z)
• Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast [91.9372563527801]
  We present Pangu-Weather, a deep learning based system for fast and accurate global weather forecast. For the first time, an AI-based method outperforms state-of-the-art numerical weather prediction (NWP) methods in terms of accuracy. Pangu-Weather supports a wide range of downstream forecast scenarios, including extreme weather forecast and large-member ensemble forecast in real-time.
  arXiv  Detail & Related papers  (2022-11-03T17:19:43Z)
• Deep Learning for Global Wildfire Forecasting [1.6929753878977016]
  We create a global fire dataset and demonstrate a prototype for predicting the presence of global burned areas on a sub-seasonal scale. We present an open-access global analysis-ready datacube, which contains a variety of variables related to the seasonal and sub-seasonal fire drivers. We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
  arXiv  Detail & Related papers  (2022-11-01T15:39:01Z)
• Multi-time Predictions of Wildfire Grid Map using Remote Sensing Local Data [0.0]
  This paper proposes a distributed learning framework that shares local data collected in ten locations in the western USA throughout local agents. The proposed model has distinct features that address the characteristic need in prediction evaluations, including dynamic online estimation and time-series modeling.
  arXiv  Detail & Related papers  (2022-09-15T22:34:06Z)
• Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data [86.1450118623908]
  We investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) We forecast the North Atlantic-European weather regimes during extended boreal winter for 1 to 15 days into the future. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5-6 days.
  arXiv  Detail & Related papers  (2022-02-10T11:37:00Z)
• Modeling Wildfire Perimeter Evolution using Deep Neural Networks [0.0]
  We propose a wildfire spreadingmodel that predicts the evolution of the wildfire perimeter in 24 hour periods. The model is able to learn wildfirespreading dynamics from real historic data sets from wildfires in the Western Sierra Nevada Mountains in Cal-ifornia.
  arXiv  Detail & Related papers  (2020-09-08T20:06:01Z)
• A generative adversarial network approach to (ensemble) weather prediction [91.3755431537592]
  We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe. The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
  arXiv  Detail & Related papers  (2020-06-13T20:53:17Z)

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.