Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)

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Application of AI to Filter Anomalous Data from Sensors in an Online Water Quality Monitoring System

Authors
Heru Dwi Wahyono1, *, Satmoko Yudo1
1Research Center For Environmental and Clean Technology, National Research and Innovation Agency, Jakarta, Indonesia
*Corresponding author. Email: heru009@brin.go.id
Corresponding Author
Heru Dwi Wahyono
Available Online 28 December 2022.
DOI
10.2991/978-94-6463-086-2_83How to use a DOI?
Keywords
Online water quality monitoring; Multiprobe water quality sensors; Artificial intelligence on water quality monitoring; Anomalous data filtering algorithms; Data quality assurance
ABSTRACT

The multiprobe sensor technology used in online water quality monitoring systems can produce water quality measurement data from several parameters at once quickly and in large quantities. The accuracy of the data is highly dependent on the quality of the river water being monitored and the performance of the probe on the sensor used. The worse the water quality and the decreased performance of the sensor probe, cause the reading of data by the sensor can produce inappropriate anomalous data. Anomalous data can cause water quality analysis to be invalid, therefore we need an Artificial Intelligence (AI) application to filter anomalous data by developing computational algorithms that can provide learning for computers to identify data generated and sent to data center servers. The algorithm method was developed using several mathematical logic models using real conditions and existing needs both in terms of regulations and historical data on water quality monitoring. By applying this algorithm, the computer can have artificial intelligence in analyzing data more accurately with monitoring data that matches the range of sensor measurement capabilities, so this method can guarantee the quality of online monitoring data.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)
Series
Advances in Biological Sciences Research
Publication Date
28 December 2022
ISBN
10.2991/978-94-6463-086-2_83
ISSN
2468-5747
DOI
10.2991/978-94-6463-086-2_83How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Heru Dwi Wahyono
AU  - Satmoko Yudo
PY  - 2022
DA  - 2022/12/28
TI  - Application of AI to Filter Anomalous Data from Sensors in an Online Water Quality Monitoring System
BT  - Proceedings of the International Conference on Sustainable Environment, Agriculture and Tourism (ICOSEAT 2022)
PB  - Atlantis Press
SP  - 623
EP  - 631
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-086-2_83
DO  - 10.2991/978-94-6463-086-2_83
ID  - Wahyono2022
ER  -