Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning

• URL: http://arxiv.org/abs/2407.15878v1
• Date: Sat, 20 Jul 2024 02:00:13 GMT
• Title: Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning
• Authors: Ayoub Jadouli, Chaker El Amrani,
• Abstract summary: This paper presents a novel approach in wildfire prediction through the integration of multisource ensemble data, including satellite data algorithms and the application of deep learning techniques. The key focus is on understanding the significance of weather sequences human activities, and specific weather parameters in wildfire prediction.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a novel approach in wildfire prediction through the integration of multisource spatiotemporal data, including satellite data, and the application of deep learning techniques. Specifically, we utilize an ensemble model built on transfer learning algorithms to forecast wildfires. The key focus is on understanding the significance of weather sequences, human activities, and specific weather parameters in wildfire prediction. The study encounters challenges in acquiring real-time data for training the network, especially in Moroccan wildlands. The future work intends to develop a global model capable of processing multichannel, multidimensional, and unformatted data sources to enhance our understanding of the future entropy of surface tiles.

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