ISSN :2582-9793

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

Original Research (Published On: 27-Sep-2024 )
Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning
DOI : https://dx.doi.org/10.54364/AAIML.2024.43152

JADOULI Ayoub and Chaker El Amrani

Adv. Artif. Intell. Mach. Learn., 4 (3):2614-2628

JADOULI Ayoub : Computer Science and Smart Systems, Faculty of Sciences and Technology, Abdelmalek Essaâdi University, Tangier, Morocco

Chaker El Amrani : Computer Science and Smart Systems, Faculty of Sciences and Technology, Abdelmalek Essaâdi University, Tangier, Morocco

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DOI: https://dx.doi.org/10.54364/AAIML.2024.43152

Article History: Received on: 06-Jul-24, Accepted on: 20-Sep-24, Published on: 27-Sep-24

Corresponding Author: JADOULI Ayoub

Email: ajadouli@uae.ac.ma

Citation: JADOULI Ayoub, Chaker El Amrani. (2024). Enhancing Wildfire Forecasting Through Multisource Spatio-Temporal Data, Deep Learning, Ensemble Models and Transfer Learning. Adv. Artif. Intell. Mach. Learn., 4 (3 ):2614-2628


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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