Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data repo...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2020-06, Vol.12 (12), p.1988
Main Authors: Rosero-Montalvo, Paul D., López-Batista, Vivian F., Riascos, Jaime A., Peluffo-Ordóñez, Diego H.
Format: Article
Language:English
Subjects:
Algorithms
Case studies
Classification
Communication
Contamination
Data analysis
Data compression
Data processing
Decision making
Embedded systems
Environmental policy
Intelligent systems
Internet of Things
Learning algorithms
Machine learning
Power consumption
Principal components analysis
prototype selection
Remote sensing
River ecology
river pollution
Rivers
Sensor arrays
Sensors
Servers
supervised classification
Turbidity
Urban renewal
Water pollution
Water quality
Wireless networks
Wireless sensor networks
WSN
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Summary:This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs12121988