Seyed-Ali-Ahmadi/FirePred
Contains codes of the paper "FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction" using environmental variables
This project helps environmental managers, emergency services, and land-use planners predict where wildfires might spread. It takes in historical wildfire data and environmental variables (like temperature, humidity, wind, and vegetation) at different time resolutions, then outputs a prediction of future wildfire spread. This helps in making informed decisions for resource allocation and evacuation planning.
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Use this if you need to forecast wildfire spread using a combination of environmental data measured at hourly, daily, and constant intervals.
Not ideal if you need a real-time wildfire prediction system, as it's built on historical data and may be influenced by specific regional environmental parameters.
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Aug 08, 2023
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