Early Detection of Wildfires with GOES-R Time Series and Deep GRU-Network

Abstract

Early detection of wildfires has been limited using the sun-synchronous orbit satellites due to their low temporal resolution and wildfires’ fast spread in the early stage. NOAA’s geostationary weather satellites GOES-R can acquire images every 15 minutes at 2km spatial resolution, and have been used for early fire detection. However, advanced processing algorithms are needed to provide timely and reliable detection of wildfires. In this research, a deep learning framework, based on Gated Recurrent Units (GRU), is proposed to detect wildfires at early stage using GOES-R dense time series data. GRU model maintains good performance on temporal modelling while keep a simple architecture, makes it suitable to efficiently process time-series data. 36 different wildfires in North and South America under the coverage of GOES-R satellites are selected to assess the effectiveness of the GRU method. The detection times based on GOES-R are compared with VIIRS active fire products at 375m resolution in NASA’s Fire Information for Resource Management System (FIRMS). The results show that GRU-based GOES-R detections of the wildfires are earlier than that of the VIIRS active fire products in most of the study areas. Also, results from proposed method offer more precise location on the active fire at early stage than GOES-R Active Fire Product in mid-latitude and low-latitude regions.

Harnessing the Power of Stacked GRU for Accurate Weather Predictions

“…The GRU model was chosen over other models for this analysis due to its advantages in weather prediction. GRU models have been widely used in various domains, including electric load forecasting [33], solar irradiance forecasting [34], precision agriculture [35], carbon dioxide concentration prediction [36], traffic prediction [37], landslide displacement prediction [38], wind speed and temperature forecasting [39], wildfire detection [40], and solar radiation prediction [41]. The advantages of GRU models in weather prediction include their simplicity and ease of implementation [33], ability to capture long-term dependencies in sequential data [34], improved performance with attention mechanisms [33], and computational efficiency compared to other recurrent neural network models [37].…”