Pant, Kunal Kumar and Remya, Ajai A S and Nagaraj, Nithin
(2025)
Advancing Forest Fires Classification using Neurochaos Learning.
arXiv.
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| Abstract: |
Forest fires are among the most dangerous and unpredictable natural disasters worldwide. Forest
fire can be instigated by natural causes or by humans. They are devastating overall, and thus,
many research efforts have been carried out to predict whether a fire can occur in an area given
certain environmental variables. Many research works employ Machine Learning (ML) and Deep
Learning (DL) models for classification; however, their accuracy is merely adequate and falls short of
expectations. This limit arises because these models are unable to depict the underlying nonlinearity
in nature and extensively rely on substantial training data, which is hard to obtain. We propose
using Neurochaos Learning (NL), a chaos-based, brain-inspired learning algorithm for forest fire
classification. Like our brains, NL needs less data to learn nonlinear patterns in the training data. It
employs one-dimensional chaotic maps, namely the Generalized Lüroth Series (GLS), as neurons.
NL yields comparable performance with ML and DL models, sometimes even surpassing them,
particularly in low-sample training regimes, and unlike deep neural networks, NL is interpretable as
it preserves causal structures in the data. Random Heterogenous Neurochaos Learning (RHNL),
a type of NL where different chaotic neurons are randomnly located to mimic the randomness and
heterogeneity of human brain gives the best F1 score of 1.0 for the Algerian Forest Fires Dataset.
Compared to other traditional ML classifiers considered, RHNL also gives high precision score of
0.90 for Canadian Forest Fires Dataset and 0.68 for Portugal Forest Fires Dataset. The results obtained
from this work indicate that Neurochaos Learning (NL) architectures achieve better performance than
conventional machine learning classifiers, highlighting their promise for developing more efficient
and reliable forest fire detection systems. |
| Item Type: |
Journal Paper
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| Subjects: |
School of Natural and Engineering Sciences > Complex Systems |
| Divisions: |
Schools > Natural Sciences and Engineering |
| Date Deposited: |
27 Apr 2026 11:57 |
| Last Modified: |
27 Apr 2026 11:57 |
| Official URL: |
https://arxiv.org/abs/2510.26383 |
| Related URLs: |
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| Funders: |
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| Projects: |
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| DOI: |
https://doi.org/10.48550/arXiv.2510.26383 |
| URI: |
http://eprints.nias.res.in/id/eprint/3320 |
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