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Embedded ANN-Based Forest Fire Prediction Case Study of Algeria

Embedded ANN-Based Forest Fire Prediction Case Study of Algeria

Mohamed Merabet, Ali Kourtiche
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 18
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.291085
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MLA

Merabet, Mohamed, and Ali Kourtiche. "Embedded ANN-Based Forest Fire Prediction Case Study of Algeria." IJDAI vol.14, no.1 2022: pp.1-18. http://doi.org/10.4018/IJDAI.291085

APA

Merabet, M. & Kourtiche, A. (2022). Embedded ANN-Based Forest Fire Prediction Case Study of Algeria. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-18. http://doi.org/10.4018/IJDAI.291085

Chicago

Merabet, Mohamed, and Ali Kourtiche. "Embedded ANN-Based Forest Fire Prediction Case Study of Algeria," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-18. http://doi.org/10.4018/IJDAI.291085

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Abstract

One of the major environmental challenges is forest fires, each year millions of hectares of forest are destroyed throughout the world, resulting in economic and ecological damages, as well as the loss of human life. Therefore, predicting forest fires is of great importance for governments; However, there is still limited study on this topic in Algeria. In this paper, we present an application of artificial neural networks to predict forest fires in embedded devices. We used meteorological data obtained from wireless sensor networks. In the experimentation, nine machine learning model are compared. The findings from this study make several contributions to the current literature. First, our model is suitable for embedded and real-time training and prediction. Moreover, it should provide better performances and accurate predictions against other models.

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