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

Abstract

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.

Lockdown effects on total suspended solids concentrations in the Lower Min River (China) during COVID-19 using time-series remote sensing images

“…Rivers are important natural water resources that are responsible for supplying water to irrigation, industrial needs, and human beings ( Rosero-Montalvo et al, 2020 ). Nevertheless, the quality of river waters is under threat by increasing human activities such as urban development, industrial production, deforestation, and inappropriate use of pesticides and fertilizers, which have adverse impacts on the water quality of rivers.…”

Toward Integrated Large-Scale Environmental Monitoring Using WSN/UAV/Crowdsensing: A Review of Applications, Signal Processing, and Future Perspectives

“…with improved spatial and temporal resolution [36,124]. Given the absence of accurate biological and chemical sensors on common low-cost WSN nodes [125,126], most work in the literature has focused on the design of advanced contamination detection algorithms that properly fuse the different data collected from multiple sensors [127]. This kind of approach intrinsically suffers from increased false alarm rates, which in turn calls for more sophisticated methods that correlate the decisions with additional information coming from other external sources [128].…”

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

“…Ryecroft et al noted the major developments in the monitoring of air quality using IoT technology; however, water quality monitoring is still dependent on manual sample collection [38]. Rosero-Montalvo et al presented an intelligent WSN system that has the ability to determine the quality of water using machine learning (ML) algorithms [39]. The aim of the research is to determine the water quality of the river through the route by creating data reports into interactive interfaces for users.…”