Using iot and ml for forest fire detection, monitoring, and prediction: a literature review | [Journal of Theoretical and Applied Information Technology • 2022]

Author(s):

1. MOUNIR GRARI: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

2. MIMOUN YANDOUZI: LARI Lab., FSO, Mohammed First University,Oujda,Morocco

3. IDRISS IDRISSI: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

4. MOHAMMED BOUKABOUS: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

5. OMAR MOUSSAOUI: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

6. MOSTAFA AZIZI: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

7. MIMOUN MOUSSAOUI: MATSI Lab., ESTO, Mohammed First University,Oujda,Morocco

Abstract:

Forests are large areas gathering trees and other plants. They are so important for protecting the environment; they filter air and water, provide food and shelter for animals, and help regulate the climate. Wildfires are one of major hazards of global warming; they destroy forests and speed up the deforestation phenomenon. Other wildfires are also caused by human errors in wilderness environments. Dry vegetation fuels a wildfire's rapid ignition and spread. It is difficult to extinguish flames even with the best efforts of forest firefighters. Smoke and air pollution from wildfires may harm human health and ruin property. Forest fires are difficult to detect at time or to anticipate it, because they spread rapidly. Early-warning systems that they are more accurate are really needed. These systems could be implemented with IoT (Internet of Things), machine learning (ML), or deep learning (DL). In this paper, we focus on this direction of research and we examine literature proposals utilizing IoT and DL to detect wildfires and their spread via a comprehensive evaluation and comparison of existing works.

Page(s): 5445-5461

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 19, Year: 2022

Keywords:

IoT , Deep Learning DL , Machine learning ML , Wildfire , Forest Fire

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