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

Department of Computer Science and Automatics Salamanca, Universidad de Salamanca, 37008 Salamanca, Spain
Department of Applied Sciences, Universidad Técnica del Norte, 100150 Ibarra, Ecuador
Department of Engineering, Universidad Mariana, 520001 Pasto, Colombia
Department of Engineering, Corporación Universitaria Autónoma de Nariño, 520002 Pasto, Colombia
School of Mathematical and Computational Sciences, Universidad Yachay Tech, 100650 Urcuquí, Ecuador
SDAS Researh Group, 100150 Ibarra, Ecuador
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1988;
Received: 24 April 2020 / Revised: 9 June 2020 / Accepted: 12 June 2020 / Published: 20 June 2020
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. View Full-Text
Keywords: prototype selection; river pollution; supervised classification; WSN prototype selection; river pollution; supervised classification; WSN
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MDPI and ACS Style

Rosero-Montalvo, P.D.; López-Batista, V.F.; Riascos, J.A.; Peluffo-Ordóñez, D.H. Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador). Remote Sens. 2020, 12, 1988.

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