Machine learning based soft sensor model for BOD estimation using intelligence at edge
Open accessAbstract
Abstract Real-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.
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Finding a suitable medium among cost and energy limitations and the data required to size the sensor network is a hurdle.
“Finding a suitable medium among cost and energy limitations and the data required to size the sensor network is also a hurdle [56,34,48,57].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [34]
The Motivation section identifies sensor cost as a limitation, and Proposed methodology says that installing more sensors increases the amount of data and the setup cost. Neither passage addresses choosing a suitable medium under energy constraints or determining the data needed to size a sensor network. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredWith various copyrighted software or sensor applications, the data might be closely connected to tools, and information might consequently be omitted at the point of software interaction.
“Additionally, with various copyrighted software or sensor applications [34], the data might be closely connected to tools, and information might consequently be omitted at the point of software interaction [35].”
From Reinvigorating algal cultivation for biomass production with digital twin technology - a smart sustainable infrastructure (2024) · cites this paper as [34]
The Introduction and Motivation discuss soft sensors and their applications in water-quality monitoring, supporting the sensor-applications portion attributed to [34]. They do not describe the software or applications as copyrighted. The statement about information being omitted is attached to [35], not this source. (LLM, conf=0.82)
Experimental — accuracy not yet measured
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- Authors
- Pattnaik, B. S., Pattanayak, A. S., Udgata, S. K., & Panda, A. K.
- Affiliations
- Biju Patnaik University of Technology (IN)
- University of Hyderabad (IN)
- National Institute of Science and Technology (IN)
- Corresponding author
- Udgata, Siba Kumar
- Venue
- Complex & Intelligent Systems
- Published
- 2021-01-07
- Volume
- 7
- Issue
- 2
- Pages
- 961-976
- ISSN
- 2198-6053 (ISSN-L)
- 2199-4536
- OpenAlex
- W3120508696
- Cited by
- 61
- References
- 56
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 15 · 2023: 9 · 2022: 10 · 2021: 1
- FWCI
- 5.30
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Complex & Intelligent Systems (publishedVersion, cc-by)
- Funders
- WOS-A
- Indo-US Science and Technology Forum
- ITRA, Ministry of Electronics and Information technology
- Topics
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Environmental Engineering › Hydrological Forecasting Using AI
- Keywords
- soft sensors, water quality monitoring, edge computing, Internet of Things, edge-cloud computing, wastewater treatment plant effluent, real-time prediction, computational performance comparison
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