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

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.

Can machine learning accelerate process understanding and decision‐relevant predictions of river water quality?

“…Examples of recent efforts to incorporate DL approaches for these predictions include the use of an LSTM to predict DO concentrations in 506 pristine U.S. catchments achieving moderate accuracy (Zhi et al, 2021), and an LSTM paired with a CNN (that generated streamflow estimates) to predict total nitrogen, phosphorus, and organic carbon in a major Korean river basin (Baek et al, 2020). LSTM, RF, and hybrid ML models have also been used for short‐term predictions of a suite of water quality variables (e.g., DO, pH, conductivity, turbidity, nutrients, water quality indices) with high‐frequency monitoring; in some cases classical ML surrogate models are used as soft sensors to make predictions of variables that are difficult or laborious to measure directly such as those that require laboratory sample analysis (Bui et al, 2020; Green et al, 2021; Harrison et al, 2021; Liu et al, 2019; Lu & Ma, 2020; Paepae et al, 2021).…”

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

“…Other ML approaches (e.g., active or reinforcement learning) combined with edge computing could be used to guide autonomous instrumentation in near realtime to collect optimal observations that capture processes of interest, such as during an anomalous event or across the spatial gradients encountered at interfaces or ecological control points (i.e., hot spots). Surrogate ML models serving as "soft sensors or electronic noses" can also be used to make predictions of variables that are difficult to measure directly using proxy measurements of easily observable variables (Paepae, Bokoro, and Kyamakya 2021). Finally, ML approaches can be used to automate quality assurance and quality control (QA/QC) of data streams in near real time moving beyond current semi-automated statistical and rule-based methods.…”

A Virtual Sensing Concept for Nitrogen and Phosphorus Monitoring Using Machine Learning Techniques

“…However, recent increases in municipal and industrial wastewater releases, which in some cases are only partially treated, and the increased production of nutrients to support agricultural fertilization have dramatically increased their presence in water bodies [ 11 ]. Consequently, the commonly applied monitoring approach that relies on analyzing grab samples in laboratories is no longer effective, particularly in terms of analysis costs and delays in data acquisition [ 13 ]. These drawbacks highlight the need for cost-effective, reliable, and accurate sensors for continuous in-situ monitoring of these nutrients.…”