Particle Swarm-Based Federated Learning Approach for Early Detection of Forest Fires

Y. Supriya and Thippa Reddy Gadekallu ()
Additional contact information
Y. Supriya: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India
Thippa Reddy Gadekallu: School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India

Sustainability, 2023, vol. 15, issue 2, 1-19

Abstract: Forests are a vital part of the ecological system. Forest fires are a serious issue that may cause significant loss of life and infrastructure. Forest fires may occur due to human or man-made climate effects. Numerous artificial intelligence-based strategies such as machine learning (ML) and deep learning (DL) have helped researchers to predict forest fires. However, ML and DL strategies pose some challenges such as large multidimensional data, communication lags, transmission latency, lack of processing power, and privacy concerns. Federated Learning (FL) is a recent development in ML that enables the collection and process of multidimensional, large volumes of data efficiently, which has the potential to solve the aforementioned challenges. FL can also help in identifying the trends based on the geographical locations that can help the authorities to respond faster to forest fires. However, FL algorithms send and receive large amounts of weights of the client-side trained models, and also it induces significant communication overhead. To overcome this issue, in this paper, we propose a unified framework based on FL with a particle swarm-optimization algorithm (PSO) that enables the authorities to respond faster to forest fires. The proposed PSO-enabled FL framework is evaluated by using multidimensional forest fire image data from Kaggle. In comparison to the state-of-the-art federated average model, the proposed model performed better in situations of data imbalance, incurred lower communication costs, and thus proved to be more network efficient. The results of the proposed framework have been validated and 94.47% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of early warning systems for forest fires.

Keywords: Federated Learning; Federated Averaging; Particle Swarm Optimization; forest fires; disaster management (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/2/964/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/2/964/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:2:p:964-:d:1025577

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().