ID2S4FH:一种新型的火灾智能决策支持系统框架

IF 3 3区 农林科学 Q2 ECOLOGY Fire-Switzerland Pub Date : 2023-06-25 DOI:10.3390/fire6070248
Kanak Kumar, N. S. Rajput, Alexey V. Shvetsov, Abdu Saif, Radhya Sahal, S. Alsamhi
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引用次数: 1

摘要

现代社会和工业部门通过仓储和配送中心(sdc),如超市、商场、仓库等,为其提供服务。大量的物资储备在这里,如粮食、衣服、鞋子、药品、电子、塑料、食用油、电线/设备、石油产品、油漆材料等。由这些材料燃烧引起的火灾分为六类,即A类、B类、C类、D类、K类和f类。使用正确的阻燃剂可以更好地扑灭A类火灾。消防的一条经验法则还说,“如果你不知道是什么在燃烧,就不要灭火”。在本文中,我们提出了一个智能决策支持系统(ID2S4FH),用于在火灾危险期间生成此类SDCs的实时“火灾地图”。我们将六个基于氧化锡的气体传感器元件,一个温度和湿度传感器以及一个带有微控制器的颗粒物(PM)传感器连接在一起,以捕获环境空气的实时特征模式。我们燃烧了属于6类火灾的16种不同类型的材料,并创建了一个由2400个样本组成的数据集。然后使用在不同分析空间域中训练的各种分类器对传感器阵列响应进行预处理和分析。在分类器中,有4个分类器对80个未知测试样本的5个类别实现了“全部正确”识别,实现的最小均方误差(MSE)为2.81 × 10−3。在发生火灾时,我们提出的ID2S4FH可以生成SDCs的实时火灾地图,并帮助消防员使用适当的阻燃剂扑灭火灾。
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ID2S4FH: A Novel Framework of Intelligent Decision Support System for Fire Hazards
Modern societies and industrial sectors are serviced through storage and distribution centres (SDCs) such as supermarkets, malls, warehouses, etc. Large quantities of supplies are stocked here, e.g., food grains, clothes, shoes, pharmaceuticals, electronics, plastics, edible oils, electrical wires/equipment, petroleum products, painting materials, etc. Fires due to the burning of these materials are categorized into six classes, viz., Class A, Class B, Class C, Class D, Class K, and Class F. A fire is extinguished better when the right type of fire retardant is used. A thumb rule on firefighting also says, “never fight a fire if you do not know what is burning”. In this paper, we have proposed an Intelligent Decision Support System (ID2S4FH) to generate a real-time ‘fire-map’ of such SDCs during a fire hazard. We have interfaced six tin-oxide-based gas sensor elements, a temperature and humidity sensor, and a particulate matter (PM) sensor with microcontrollers to capture the real-time signature patterns of the ambient air. We burned sixteen different types of materials belonging to six classes of fire and created a dataset consisting of 2400 samples. The sensor array responses were then pre-processed and analysed using various classifiers trained in different analysis space domains. Among the classifiers, four classifiers achieved ‘all correct’ identification of the fire classes of 80 unknown test samples, and the lowest mean squared error (MSE) achieved was 2.81 × 10−3. During a fire hazard, our proposed ID2S4FH can generate real-time fire maps of SDCs and help firefighters to extinguish the fire using the appropriate fire retardant.
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来源期刊
Fire-Switzerland
Fire-Switzerland Multiple-
CiteScore
3.10
自引率
15.60%
发文量
182
审稿时长
11 weeks
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