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

arXiv: https://arxiv.org/abs/2407.15878 · HTML: https://mlsanity.com/paper/2407.15878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 16 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 1 of 1)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)

## Built on by (most cited first, 2 of 2)

- [Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset from Multisource Observations](https://mlsanity.com/paper/2411.06202/) (2024)
- [Deep Learning with Pretrained 'Internal World' Layers: A Gemma 3-Based Modular Architecture for Wildfire Prediction](https://mlsanity.com/paper/2504.18562/) (2025)

## Abstract (arXiv)

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.