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Early wildfire detection using machine learning model deployed in t... |
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by Mounir Grari, Idriss Idrissi, Mohammed Boukabous, Omar Moussaoui, M... |
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The impact of wildfires, even following the fire's |
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extinguishment, continues |
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to affect harmfully public health and prosperity. |
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Wildfires are becoming |
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increasingly frequent and severe, and make the world's |
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biodiversity in a |
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growing serious danger. The fires are responsible for |
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negative economic |
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consequences for individuals, corporations, and |
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authorities. Researchers are |
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developing new approaches for detecting and monitoring |
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wildfires, that |
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make use of advances in computer vision, machine |
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learning, and remote |
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sensing technologies. IoT sensors help to improve the |
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efficiency of detecting |
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active forest fires. In this paper, we propose a novel |
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approach for predicting |
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wildfires, based on machine learning. It uses a |
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regression model that we train |
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over NASA's fire information for resource management |
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system (FIRMS) |
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dataset to predict fire radiant power in megawatts. The |
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analysis of the |
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obtained simulation results (more than 99% in the R2 |
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metric) shows that the |
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ensemble learning model is an effective method for |
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predicting wildfires |
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using an IoT device equipped with several sensors that |
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could potentially |
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collect the same data as the FIRMS dataset, such as smart |
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cameras or drones. |
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Date Published: 2022-11-08 09:10:10 |
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Identifier: early-wildfire-detection-using-machine-learning-model-depl… |
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Item Size: 11826251 |
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Media Type: texts |
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# Topics |
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Edge computing; Ensemble learning; Fo... |
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