Next Article in Journal
Advancing Sequential Managed Aquifer Recharge Technology (SMART) Using Different Intermediate Oxidation Processes
Previous Article in Journal
The Human Threat to River Ecosystems at the Watershed Scale: An Ecological Security Assessment of the Songhua River Basin, Northeast China
Article Menu
Issue 3 (March) cover image

Export Article

Open AccessArticle
Water 2017, 9(3), 220; doi:10.3390/w9030220

Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream

1
Jae Sung Civil Engineering Building, Department of Civil and Environmental Engineering, Hanyang University, 222 Wasgsimini-ro, Seongdong-gu, Seoul 04763, Korea
2
Department of Civil Engineering, Faculty of Engineering and Architecture, BUITEMS, Quetta 87650, Balochistan, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: Maria Filomena Camões
Received: 29 January 2017 / Revised: 5 March 2017 / Accepted: 13 March 2017 / Published: 16 March 2017
View Full-Text   |   Download PDF [22503 KB, uploaded 16 March 2017]   |  

Abstract

In the present case study, the use of an advanced, efficient and low-cost technique for monitoring an urban stream was reported. Physicochemical parameters (PcPs) of Jungnangcheon stream (Seoul, South Korea) were assessed using an Internet of Things (IoT) platform. Temperature, dissolved oxygen (DO), and pH parameters were monitored for the three summer months and the first fall month at a fixed location. Analysis was performed using clustering techniques (CTs), such as K-means clustering, agglomerative hierarchical clustering (AHC), and density-based spatial clustering of applications with noise (DBSCAN). An IoT-based Arduino sensor module (ASM) network with a 99.99% efficient communication platform was developed to allow collection of stream data with user-friendly software and hardware and facilitated data analysis by interested individuals using their smartphones. Clustering was used to formulate relationships among physicochemical parameters. K-means clustering was used to identify natural clusters using the silhouette coefficient based on cluster compactness and looseness. AHC grouped all data into two clusters as well as temperature, DO and pH into four, eight, and four clusters, respectively. DBSCAN analysis was also performed to evaluate yearly variations in physicochemical parameters. Noise points (NOISE) of temperature in 2016 were border points (ƥ), whereas in 2014 and 2015 they remained core points (ɋ), indicating a trend toward increasing stream temperature. We found the stream parameters were within the permissible limits set by the Water Quality Standards for River Water, South Korea. View Full-Text
Keywords: Arduino monitoring; urban stream; data analysis; clustering technique; internet of things Arduino monitoring; urban stream; data analysis; clustering technique; internet of things
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Jo, B.; Baloch, Z. Internet of Things-Based Arduino Intelligent Monitoring and Cluster Analysis of Seasonal Variation in Physicochemical Parameters of Jungnangcheon, an Urban Stream. Water 2017, 9, 220.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top