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基于物联网和机器学习的河口含水层盐楔监测系统（SISME）：以玛格达莱纳河口为例。

SISME, Estuarine Monitoring System Based on IOT and Machine Learning for the Detection of Salt Wedge in Aquifers: Case Study of the Magdalena River Estuary.

机构信息

Department of Computer Science and Electronics, Universidad de la Costa CUC, Barranquilla 080002, Colombia.

Faculty of Marine Sciences, Escuela Naval de Suboficiales ARC "Barranquilla", Armada Nacional de Colombia, Barranquilla 080002, Colombia.

出版信息

Sensors (Basel). 2021 Mar 29;21(7):2374. doi: 10.3390/s21072374.

DOI:10.3390/s21072374
PMID:33805544
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8036609/
Abstract

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project "Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary".

摘要

本文包含了一种基于物联网和机器学习技术开发应用程序的方法、结果和分析，该应用程序可用于支持识别哥伦比亚 Magdalena 河口的盐水楔。通过这项研究，展示了在河口安装最合适的电信架构的过程，以及开发的名为 SISME（河口监测系统）的软件的特点，以及在基于时间序列的预测技术实施后获得的结果。该实施支持了 Barranquilla 港口的海上安全，因为它可以支持与河口相关的决策。这项研究是项目“实施温度、电导率和压力传感器的无线系统，以支持识别盐水楔及其对 Magdalena 河口海上安全的影响”的结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/6bb63cc4d384/sensors-21-02374-g009.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/3d804b20c6ff/sensors-21-02374-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/072bb7ba4e45/sensors-21-02374-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/6bb63cc4d384/sensors-21-02374-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/ec20c2a785ca/sensors-21-02374-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/1eaa0ad9687a/sensors-21-02374-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/3eff3d0b117e/sensors-21-02374-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/9def1ed58311/sensors-21-02374-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/f429e3a6953a/sensors-21-02374-g006.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/072bb7ba4e45/sensors-21-02374-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7ccb/8036609/6bb63cc4d384/sensors-21-02374-g009.jpg

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