Comparisons in Drinking Water Systems Using K-Means and A-Priori to Find Pathogenic Bacteria Genera
Comparisons in Drinking Water Systems Using K-Means and A-Priori to Find Pathogenic Bacteria Genera
复制标题
使用 K-Means 和 A-Priori 查找致病菌属的饮用水系统比较
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
D. Haar
中科院分区：
文献类型：
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作者：
Tevin Moodley;D. Haar
由于水资源变得有限，与水传播的病原体有关的疾病病例增加，因此需要对地下水等替代水源和地表水等普通水源进行心理研究和调查，以确保提供给用户的水可以安全饮用。本研究以16S rRNA图谱为基础，利用K-均值和A先验等机器学习方法，对饮用水系统地下水源水和地表水源水中的细菌属进行了比较。16S可用于细菌属的鉴定和鉴别。不仅重要的是确定在水源中发现的特定细菌属，而且需要检查相对丰度，以确定地下水是否是比地表水更可行的饮用水来源。利用南非各地最近报告的通过水传播的疾病事件，可以确定确定水质和安全的五个关键细菌指标，这些指标可以在地下水和地表水中找到。从采集的样本中捕获的数据被用来以更有效率和更有效的方式确定每个水样中每种细菌的丰度。为该项目概述的五项指标是：大肠杆菌(Escherichia)、军团菌、血友病、蜈蚣弧菌、链球菌。所使用的数据集包含来自地下水和地表水的细菌，使用维度技术和许多参数可以减少，以便更有效地处理。所使用的算法包括K-Means算法用于对数据进行聚类以便于解释，先验算法用于获取频繁项以生成关联规则，从而实现模式，支持向量机用于预测新数据进入流中的错误。利用算法产生的结果，发现在地下水中发现的病原体的平均相对丰度高于在地表水中发现的病原体的平均相对丰度。结果表明，使用所提出的方法进行自动化、可扩展的水可行性评估是可行的，这使得随着物联网(IoT)在该领域的发展，水可行性评估成为一个有吸引力的研究途径。
As water resources have become limited, there have been increased cases in illnesses related to waterborne pathogens, with this is mind studies and investigation needs to be done on alternative water sources such as, ground water and common water sources such as surface waters, to ensure that water provided to consumers are safe to consume. This research paper compares bacterial genera in both ground and surface source waters for drinking water systems, based on 16S rRNA profiling using machine learning methods, such as K-means and A priori. 16S can be used to identify and differentiate between bacterial genera. Not only is it important to identify specific bacterial genera found in water sources, but the relative abundance needs to be examined to determine whether groundwater is a more viable drinking water source than surface water. Using recent incidences of water-borne illnesses that have been reported across South Africa, five key bacterial indicators to determine water quality and safety can be identified, which can be found in both groundwater and surface waters. Captured data from samples collected is used to determine the abundance of each bacterium for each water sample in a more efficient and effective manner the five indicators outlined for this project are; E. coli (Escherichia), Legionella, Hemophilia, Bdellovibrio, Streptococcus. The dataset, used contained bacterium from both ground and surface waters using dimensional techniques and many parameters can be reduced for more efficient processing. The algorithms used include K-Means to cluster the data to allow for interpretation, A Priori algorithm to get the frequent items so that association rules can be derived, which allows patterns to be realized and SVM (support vector machine) to predict the error of new data coming into a stream. Using the results produced by the algorithms, it was discovered that the mean relative abundance of the pathogenic organisms found in groundwater was higher than that found in surface water. Results indicated that automated, scalable water viability assessment is feasible using the methods proposed, which make it an attractive avenue of research as the Internet of Things (IoT) in this domain develops.