Department of Electrical, Electronic and Telecommunication Engineering, Faculty of Engineering, General Sir John Kotelawala Defence University, Ratmalana, 10390, Sri Lanka.
Sci Rep. 2022 Jan 7;12(1):46. doi: 10.1038/s41598-021-03882-9.
Forest fires have become a major threat around the world, causing many negative impacts on human habitats and forest ecosystems. Climatic changes and the greenhouse effect are some of the consequences of such destruction. Interestingly, a higher percentage of forest fires occur due to human activities. Therefore, to minimize the destruction caused by forest fires, there is a need to detect forest fires at their initial stage. This paper proposes a system and methodology that can be used to detect forest fires at the initial stage using a wireless sensor network. Furthermore, to acquire more accurate fire detection, a machine learning regression model is proposed. Because of the primary power supply provided by rechargeable batteries with a secondary solar power supply, a solution is readily implementable as a standalone system for prolonged periods. Moreover, in-depth attention is given to sensor node design and node placement requirements in harsh forest environments and to minimize the damage and harmful effects caused by wild animals, weather conditions, etc. to the system. Numerous trials conducted in real tropical forest sites found that the proposed system is effective in alerting forest fires with lower latency than the existing systems.
森林火灾已成为全球主要威胁，对人类栖息地和森林生态系统造成许多负面影响。气候变化和温室效应是这种破坏的一些后果。有趣的是，由于人类活动，森林火灾发生的比例更高。因此，为了将森林火灾造成的破坏降到最低，需要在火灾初始阶段进行检测。本文提出了一种使用无线传感器网络在初始阶段检测森林火灾的系统和方法。此外，为了更准确地检测火灾，提出了一种机器学习回归模型。由于主要由可充电电池提供电源，并且有备用的太阳能电源，因此可以很容易地作为独立系统实现长时间运行。此外，还深入关注了在恶劣的森林环境中传感器节点设计和节点放置要求，以尽量减少野生动物、天气条件等对系统造成的损坏和有害影响。在真实的热带森林地点进行的多次试验表明，与现有系统相比，所提出的系统在发出火灾警报时具有更低的延迟。
