Abstract
In the past two decades, sensor technology has achieved the manufacturing of low-cost and portable sensors that can be used for different environmental applications such as water quality monitoring, air quality monitoring, and soil quality monitoring. The sensors used for environmental monitoring face the problem of drift sooner or later after installation. The drift may occur due to sensor aging, temperature and humidity variation, poisoning among the sensor array, or due to a combination of all. This analysis will lead us to a different track. This sensor drift will demolish the calibration model of any instrument. This issue can be solved by the calibration of the sensors, which is also a challenge for field-deployable instruments. In this chapter, an alternate solution is provided for the drift compensation based on artificial neural network (ANN). A low-cost pH sensor is used for the research work and explanation as well. The pH sensor readings were observed 66 times during the measurement session in the reference solution. The drift was observed in the pH sensor readings and compensated using a feed-forward neural network. The simulation was performed on the Python platform. The drift compensation was successfully achieved using the ANN model as the RMSE was reduced to as minimum of 0.0001%.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
L.Y. Li, H. Jaafar, N.H. Ramli, Preliminary study of water quality monitoring based on WSN technology. In: 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018 (Institute of Electrical and Electronics Engineers Inc., 2018)
World Health Organization (WHO) (1996) WHO | Drinking Water. Fact sheet No. 391. World Health Organization. 2017. Available from: https://www.who.int/mediacentre/factsheets/fs391/en/
N. Vijayakumar, R. Ramya, The real time monitoring of water quality in IoT environment. in 2015 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS) (IEEE, 2015), pp. 1–5
M. Padilla, A. Perera, I. Montoliu et al., Drift compensation of gas sensor array data by orthogonal signal correction. Chemom. Intell. Lab. Syst. 100, 28–35 (2010). https://doi.org/10.1016/j.chemolab.2009.10.002
K. Yan, D. Zhang, Correcting instrumental variation and time-varying drift: a transfer learning approach with autoencoders. IEEE Trans. Instrum. Meas. 65, 2012–2022 (2016). https://doi.org/10.1109/TIM.2016.2573078
S. Liu, L. Feng, J. Wu et al., Concept drift detection for data stream learning based on angle optimized global embedding and principal component analysis in sensor networks. Comput. Electr. Eng. 58, 327–336 (2017). https://doi.org/10.1016/j.compeleceng.2016.09.006
V. Panchuk, L. Lvova, D. Kirsanov et al., Extending electronic tongue calibration lifetime through mathematical drift correction: Case study of microcystin toxicity analysis in waters. Sens. Actuators B Chem 237, 962–968 (2016). https://doi.org/10.1016/J.SNB.2016.07.045
T. Artursson, T. Eklov, I. Lundstrom et al., Drift correction for gas sensors using multivariate methods. J. Chemom. 14, 711–723 (2000). https://doi.org/10.1002/1099-128X(200009/12)14:5/6%3c711::AID-CEM607%3e3.0.CO;2-4
A. Ziyatdinov, S. Marco, A. Chaudry et al., Drift compensation of gas sensor array data by common principal component analysis. Sens. Actuators, B Chem. (2010). https://doi.org/10.1016/j.snb.2009.11.034
T Mitchell, Chapter 06. Mach Learn (1997). https://doi.org/10.1007/s10994-009-5101-2
L.A. Gatys, A.S. Ecker, M. Bethge, A Neural Algorithm of Artistic Style (2015)
R. Bhardwaj, S. Majumder, P.K. Ajmera, et al., Temperature compensation of ISFET based pH sensor using artificial neural networks. in Proceedings of the 2017 IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2017 (2017)
P. Khatri, K. Kumar Gupta, R. Kumar Gupta, Raspberry Pi-based smart sensing platform for drinking-water quality monitoring system: a python framework approach. Drink Water Eng. Sci. 12, 31–37 (2019). https://doi.org/10.5194/dwes-12-31-2019
scikit-learn: machine learning in Python—scikit-learn 0.22 documentation. https://scikit-learn.org/stable/index.html. Accessed 13 Dec 2019
Root-mean-square deviation—Wikipedia. https://en.wikipedia.org/wiki/Root-mean-square_deviation. Accessed 16 Dec 2019
P. Khatri, K.K. Gupta, R.K. Gupta, Assessment of water quality parameters in real-time environment. SN Comput. Sci. 1, 340 (2020). https://doi.org/10.1007/s42979-020-00368-9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Khatri, P., Gupta, K.K., Gupta, R.K. (2021). Drift Compensation of a Low-Cost pH Sensor by Artificial Neural Network. In: Bansal, J.C., Paprzycki, M., Bianchini, M., Das, S. (eds) Computationally Intelligent Systems and their Applications. Studies in Computational Intelligence, vol 950. Springer, Singapore. https://doi.org/10.1007/978-981-16-0407-2_8
Download citation
DOI: https://doi.org/10.1007/978-981-16-0407-2_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0406-5
Online ISBN: 978-981-16-0407-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)