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

Ayoub Jadouli, Chaker El Amrani

TL;DR

This paper addresses wildfire forecasting by fusing multisource spatiotemporal data, including satellite imagery, with deep learning. It introduces an ensemble model built on transfer learning to forecast fires, leveraging LSTM for weather sequences, CNN for human activity, and ground-data signals. The results indicate robust performance across regions and underscore the benefits of data fusion and transfer learning, while noting challenges from data scarcity and overfitting in limited settings. The work highlights the potential for a global, multichannel predictive framework and suggests avenues to study surface-tile entropy to deepen environmental understanding and forecasting capabilities.

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.

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

TL;DR

This paper addresses wildfire forecasting by fusing multisource spatiotemporal data, including satellite imagery, with deep learning. It introduces an ensemble model built on transfer learning to forecast fires, leveraging LSTM for weather sequences, CNN for human activity, and ground-data signals. The results indicate robust performance across regions and underscore the benefits of data fusion and transfer learning, while noting challenges from data scarcity and overfitting in limited settings. The work highlights the potential for a global, multichannel predictive framework and suggests avenues to study surface-tile entropy to deepen environmental understanding and forecasting capabilities.

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.
Paper Structure (28 sections, 1 figure)

This paper contains 28 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Previous Methods of Wildfire Forecasting
  4. Use of Satellite Data in Wildfire Prediction
  5. Application of Deep Learning and Ensemble Models in Environmental Sciences
  6. The Role of Transfer Learning in Predictive Modeling
  7. Methodology
  8. Data Sources
  9. Weather Forecasting Using LSTM
  10. Human Activity Detection Using CNN
  11. Ground Data Integration
  12. Ensemble Model and Transfer Learning
  13. Results and Discussion
  14. Model Performance
  15. Insights from Multisource Data
  16. ...and 13 more sections

Figures (1)

  • Figure 1: Schema of Proposed Models for Wildfire Prediction System