MOSQUITO EDGE: An Edge-Intelligent Real-Time Mosquito Threat Prediction Using an IoT-Enabled Hardware System.
MOSQUITO EDGE: An Edge-Intelligent Real-Time Mosquito Threat Prediction Using an IoT-Enabled Hardware System.
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DOI：
10.3390/s22020695
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发表时间：
2022-01-17
期刊：
影响因子：
--
通讯作者：
Sundaravadivel P
中科院分区：
文献类型：
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作者：
Polineni S;Shastri O;Bagchi A;Gnanakumar G;Rasamsetti S;Sundaravadivel P
利用气候变量进行二元预测的物种分布模型是当前和未来气候情景生态位预测的有效工具。在这项研究中，一个哈钦森超体积被定义为从国家海洋和大气管理局（NOAA）收集的温度，湿度，气压，降水和云量气候矢量，这些矢量与从NASA的公民科学平台地球仪Observer和国家生态观测网络中提取的蚊子存在和不存在点相匹配。创建了一个基于二进制分类的86%准确率的随机森林模型来预测蚊子的威胁。给定位置和日期输入，该模型根据投票支持存在标签的决策树的数量产生威胁级别。特征重要性图和回归显示湿度与蚊子威胁之间以及温度与低于28 °C阈值的蚊子威胁之间存在正线性相关性。根据统计分析和生态智慧，发现温暖潮湿地区的高威胁集群和寒冷干燥地区的低威胁集群。通过在云上和ArcGIS仪表板中运行模型，可以在任何纬度和经度上进行准确和粒度的实时威胁级别预测。开发了一种利用全球定位系统（GPS）智能手机技术和物联网（IoT）在边缘收集和分析数据的设备。来自边缘设备的数据沿着相应的日期和位置被自动输入到上述随机森林模型中，以向用户提供实时威胁级别预测。这种廉价的硬件可用于受病媒传播疾病威胁的发展中国家或没有云连接的偏远地区。这些设备可以与公民科学蚊子数据平台相链接，为基于机器学习的SDM构建训练数据集。
Species distribution models (SDMs) that use climate variables to make binary predictions are effective tools for niche prediction in current and future climate scenarios. In this study, a Hutchinson hypervolume is defined with temperature, humidity, air pressure, precipitation, and cloud cover climate vectors collected from the National Oceanic and Atmospheric Administration (NOAA) that were matched to mosquito presence and absence points extracted from NASA’s citizen science platform called GLOBE Observer and the National Ecological Observatory Network. An 86% accurate Random Forest model that operates on binary classification was created to predict mosquito threat. Given a location and date input, the model produces a threat level based on the number of decision trees that vote for a presence label. The feature importance chart and regression show a positive, linear correlation between humidity and mosquito threat and between temperature and mosquito threat below a threshold of 28 °C. In accordance with the statistical analysis and ecological wisdom, high threat clusters in warm, humid regions and low threat clusters in cold, dry regions were found. With the model running on the cloud and within ArcGIS Dashboard, accurate and granular real-time threat level predictions can be made at any latitude and longitude. A device leveraging Global Positioning System (GPS) smartphone technology and the Internet of Things (IoT) to collect and analyze data on the edge was developed. The data from the edge device along with its respective date and location collected are automatically inputted into the aforementioned Random Forest model to provide users with a real-time threat level prediction. This inexpensive hardware can be used in developing countries that are threatened by vector-borne diseases or in remote areas without cloud connectivity. Such devices can be linked with citizen science mosquito data platforms to build training datasets for machine learning based SDMs.
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影响因子：
6.7
作者：
Kalluri S;Gilruth P;Rogers D;Szczur M
通讯作者：
Szczur M
影响因子：
8
作者：
O'Brien, Robert;Ishwaran, Hemant
通讯作者：
Ishwaran, Hemant
DOI：
10.1371/currents.outbreaks.90e80717c4e67e1a830f17feeaaf85de
发表时间：
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期刊：
PLoS currents
影响因子：
--
作者：
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通讯作者：
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影响因子：
3.8
作者：
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通讯作者：
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影响因子：
2.6
作者：
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通讯作者：
Lobaton, Edgar