Wildfire Prediction and Visualization: A Machine Learning Approach Using U.S. Data

DOI

10.2991/978-94-6463-948-3_9How to use a DOI?

Keywords

wildfire; frequency; web application

Abstract

Wildfires cause extensive damage each year in the United States, impacting lives, property, and ecosystems. This paper presents an exploratory visualization and machine learning approach to under- stand and predict wildfire causes using a large-scale dataset of 1.88 mil- lion U.S. wildfire incidents. We clean and analyze the dataset to reveal trends in wildfire frequency, distribution, and causation. Using Random Forest classifiers, we develop models to predict fire causes, achieving an accuracy of around 65%. A cloud-deployed web application integrates these models, allowing users to input specific parameters to visualize wildfire data and perform predictive analyses. This interactive platform offers a dynamic tool for real-time wildfire insights, aiding in risk assess- ment and potential preventative strategies….

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bhavana Nare
PY  - 2026
DA  - 2026/01/06
TI  - Wildfire Prediction and Visualization: A Machine Learning Approach Using U.S. Data
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 132
EP  - 151
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_9
DO  - 10.2991/978-94-6463-948-3_9
ID  - Nare2026
ER  -