Abstract
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.
Highlights
This study presented a detailed review of the feasibility of virtual sensing for real-time monitoring of surface and groundwater sources for irrigation purposes
The review provides a general guideline for virtual sensor design and discusses the fundamentals of virtual sensing, for dummies
Several variables are compared and discussed. It formulates a comprehensive specification book for real-time monitoring of water quality. This specification book presents the global system architecture of virtual sensor monitoring in an internet of things environment and the updated cost model that includes this global system architecture
Summary
Water is an essential resource for every aspect of human and ecosystem health and survival. It is important to note that WSN is a major technology enabling the IoT [19], and this integration allows the WSN to reach its full potential [17] Despite these advancements in sensor technologies, certain parameters (e.g., Escherichia coli, total phosphorus, total nitrogen, and chemical oxygen demand, among others) still require the traditional laboratory approach for analysis due to a lack of suitable sensors [20,21]. The ability of ML (a subfield of artificial intelligence) to extract useful information from an accessible historical database makes it ideal for virtual sensor applications [22,24,29] In this context, the other significant contribution of the IoT-based WQM system is that it enables the integration of ML tools in the cloud server in order to predict these hard-to-measure parameters based on surrogates measured using sensors [16]. This work fills this gap and addresses the limitations mentioned above by presenting a detailed analysis of the feasibility application of virtual sensing for online monitoring of surface and groundwater sources for a particular use case (or application)
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