Summary of Explainable Global Wildfire Prediction Models Using Graph Neural Networks, by Dayou Chen and Sibo Cheng and Jinwei Hu and Matthew Kasoar and Rossella Arcucci
Explainable Global Wildfire Prediction Models using Graph Neural Networks
by Dayou Chen, Sibo Cheng, Jinwei Hu, Matthew Kasoar, Rossella Arcucci
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel Graph Neural Network (GNN)-based model for global wildfire prediction, addressing challenges in handling missing oceanic data and long-range dependencies in meteorological data. The hybrid model combines the spatial capabilities of Graph Convolutional Networks (GCNs) with the temporal depth of Long Short-Term Memory (LSTM) networks, transforming global climate and wildfire data into a graph representation. Benchmarked against established architectures using JULES-INFERNO simulations, the model demonstrates superior predictive accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wildfires are getting worse due to climate change, so predicting where they will happen is crucial. Current models struggle with missing oceanic data and understanding patterns across distant regions. This paper presents a new way to predict wildfires by combining two types of artificial intelligence: Graph Neural Networks (GNNs) and Long Short-Term Memory (LSTM) networks. The model transforms complex climate and wildfire data into a graph, making it easier to understand and predict where fires will occur. |
Keywords
* Artificial intelligence * Gnn * Graph neural network * Lstm
