From Fully Physical to Virtual Sensing for Water Quality Assessment: A Comprehensive Review of the Relevant State-of-the-Art

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 21; p. 6971
• Main Authors: Paepae, Thulane, Bokoro, Pitshou N, Kyamakya, Kyandoghere
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 20.10.2021; MDPI
• Subjects: Agricultural production; Artificial intelligence; Chemical oxygen demand; Communication; Costs; Developing countries; Environmental monitoring; Groundwater; Industrial development; Internet of Things; irrigation water quality parameters; Laboratories; LDCs; Linear Models; low cost; Machine Learning; Mathematical models; Neural Networks, Computer; Parameters; physical sensor; Quality assessment; Real time; Review; Sensors; soft sensor; specification book; Urbanization; Virtual sensors; Water pollution; Water Quality; Water shortages; specification book; deep learning; internet of things; review; soft sensor; machine learning; measurement accuracy; irrigation water quality parameters; low cost; physical sensor
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s21216971

Rapid urbanization, industrial development, and climate change have resulted in water pollution and in the quality deterioration of surface and groundwater at an alarming rate, deeming its quick, accurate, and inexpensive detection imperative. Despite the latest developments in sensor technologies, real-time determination of certain parameters is not easy or uneconomical. In such cases, the use of data-derived virtual sensors can be an effective alternative. In this paper, the feasibility of virtual sensing for water quality assessment is reviewed. The review focuses on the overview of key water quality parameters for a particular use case and the development of the corresponding cost estimates for their monitoring. The review further evaluates the current state-of-the-art in terms of the modeling approaches used, parameters studied, and whether the inputs were pre-processed by interrogating relevant literature published between 2001 and 2021. The review identified artificial neural networks, random forest, and multiple linear regression as dominant machine learning techniques used for developing inferential models. The survey also highlights the need for a comprehensive virtual sensing system in an internet of things environment. Thus, the review formulates the specification book for the advanced water quality assessment process (that involves a virtual sensing module) that can enable near real-time monitoring of water quality.