LGHCIRMay 23, 2021

RtFPS: An Interactive Map that Visualizes and Predicts Wildfires in the US

arXiv:2105.10880v24 citations
Originality Synthesis-oriented
AI Analysis

This tool addresses wildfire risk visualization for the general public and stakeholders, but it is incremental as it applies existing ML methods to new data without novel methodological breakthroughs.

The authors tackled the problem of increasing wildfires in the US by developing RtFPS, a real-time prediction system that visualizes wildfire risk using a machine learning model, achieving a focus on interactive mapping of historical events and environmental data.

Climate change has largely impacted our daily lives. As one of its consequences, we are experiencing more wildfires. In the year 2020, wildfires burned a record number of 8,888,297 acres in the US. To awaken people's attention to climate change, and to visualize the current risk of wildfires, We developed RtFPS, "Real-Time Fire Prediction System". It provides a real-time prediction visualization of wildfire risk at specific locations base on a Machine Learning model. It also provides interactive map features that show the historical wildfire events with environmental info.

Code Implementations1 repo
Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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