AI-Based Principal Component Analysis (PCA) Approach for the Determination of Key Water Quality Parameters
Keywords:
Artificial Intelligence, Machine Learning, Water quality parameters, Aqua farming, AquacultureAbstract
The health of the fish is impacted by several water quality indicators, including pH, Dissolved Oxygen, Unionized Ammonia, Turbidity, etc. Therefore, research has been conducted utilizing a machine learning approach to determine the key variables that have the greatest impact on fish. To recognize this, a new classification system called Fish Classification Index (FCI) is developed to classify fish environments. In this approach, the water body ecology is divided into four groups: Alive, Disaster, Just to Dwell (JTD), and Just to Maintain (JTM). A mathematical model is developed based on this Fish Classification Index. Around ten water quality criteria are considered for the calculation in this paper. Additionally, it's critical to comprehend which factor has the greatest impact on fish health and the degree to which it does so. Therefore, Principal Component Analysis (PCA), a statistical method, is utilized in Artificial Intelligence (AI) to investigate the key variables that affect the Fish Classification Index's judgment. Using this technique, it was observed that pH and DO each have a 22% and 32% influence on the decision, respectively. Overall, it has been discovered that four parameters account for about 80% of decision-making, with the remaining six parameters present in water accounting for 20% of decision-making. The Identification and comprehension of the most important factors from the current research will help the relevant stakeholders to act appropriately in maintaining a healthy water body ecology. Additionally, it aids engineering designers in making appropriate use of the right sensors, leading to efficient resource use.
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S. Ndlovu, S. Moyo, S. Nleya and S. Dube, "Precision Fish Farming Systems: A Mapping Study," 2022 1st Zimbabwe Conference of Information and Communication Technologies (ZCICT), Harare, Zimbabwe, 2022, pp. 1-5, doi: 10.1109/ZCICT55726.2022.10045996.
H. F. Hawari and M. A. Hazwan, "Development of Iot Monitoring System For Aquaculture Application," 2022 International Conference on Green Energy, Computing and Sustainable Technology (GECOST), Miri Sarawak, Malaysia, 2022, pp. 330-334, doi: 10.1109/GECOST55694.2022.10010661.
H. Yuan et al., "Evaluation of Smart Environmental Protection Systems and Novel UV-Oriented Solution for Integration, Resilience, Inclusiveness and Sustainability," 2020 5th International Conference on Universal Village (UV), Boston, MA, USA, 2020, pp. 1-77, doi: 10.1109/UV50937.2020.9426222.
S. Verma and Prachi, "Wireless Sensor Network application for water quality monitoring in India," 2012 NATIONAL CONFERENCE ON COMPUTING AND COMMUNICATION SYSTEMS, Durgapur, India, 2012, pp. 1-5, doi: 10.1109/NCCCS.2012.6412990.
L. Yunfeng and Z. Tianpei, "A Design of Dissolved Oxygen Monitoring System Based on Nb-Iot," 2019 International Conference on Smart Grid and Electrical Automation (ICSGEA), Xiangtan, China, 2019, pp. 98-101, doi: 10.1109/ICSGEA.2019.00030.
N. Shaghaghi, T. Nguyen, J. Patel, A. Soriano and J. Mayer, "DOxy: Dissolved Oxygen Monitoring," 2020 IEEE Global Humanitarian Technology Conference (GHTC), Seattle, WA, USA, 2020, pp. 1-4, doi: 10.1109/GHTC46280.2020.9342916.
J. A. Cruz-Anchiraico, L. N. Mantari-Ramos, J. D. A. Cangalaya and D. Huamanchahua, "Design of an Automated System of PH and Water Level for an Aquaponic Module," 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, NY, USA, 2022, pp. 0603-0608, doi: 10.1109/UEMCON54665.2022.9965721.
Gholizadeh, Mohammad, et al. “A Comprehensive Review on Water Quality Parameters Estimation Using Remote Sensing Techniques.” Sensors, vol. 16, no. 8, MDPI AG, Aug. 2016, p. 1298. Crossref, https://doi.org/10.3390/s16081298.
S. R. Khaire and R. M. Wahul, "Water Quality Data Analysis and Monitoring System in IoT Environment," 2018 3rd International Conference on Contemporary Computing and Informatics (IC3I), Gurgaon, India, 2018, pp. 326-330, doi: 10.1109/IC3I44769.2018.9007289.
Kumar, Ajitesh. “Supervised and Unsupervised Learning Difference - Data Analytics.” Data Analytics, 2 May 2023, vitalflux.com/dummies-notes-supervised-vs-unsupervised-learning.
A. Singh, N. Thakur and A. Sharma, "A review of supervised machine learning algorithms," 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India, 2016, pp. 1310-1315.
F. Setievi, J. Natalia, T. R. Tjhang, I. S. Edbert and D. Suhartono, "A Comparative Study of Supervised Machine Learning Algorithms for Fake Review Detection," 2022 5th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 2022, pp. 306-312, doi: 10.1109/ISRITI56927.2022.10052860.
N. Amruthnath and T. Gupta, "A research study on unsupervised machine learning algorithms for early fault detection in predictive maintenance," 2018 5th International Conference on Industrial Engineering and Applications (ICIEA), Singapore, 2018, pp. 355-361, doi: 10.1109/IEA.2018.8387124.
R. B. Shrestha, M. Razavi and P. W. C. Prasad, "An Unsupervised Machine Learning Technique for Recommendation Systems," 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia, 2020, pp. 1-9, doi: 10.1109/CITISIA50690.2020.9371817.
C. Yumeng and F. Yinglan, "Research on PCA Data Dimension Reduction Algorithm Based on Entropy Weight Method," 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Taiyuan, China, 2020, pp. 392-396, doi: 10.1109/MLBDBI51377.2020.00084.
Sarker, Iqbal H. “Machine Learning: Algorithms, Real-World Applications and Research Directions.” SN Computer Science, vol. 2, no. 3, Springer Science and Business Media LLC, Mar. 2021. Crossref, https://doi.org/10.1007/s42979-021-00592-x.
Y. Khan and C. S. See, "Predicting and analyzing water quality using Machine Learning: A comprehensive model," 2016 IEEE Long Island Systems, Applications and Technology Conference (LISAT), Farmingdale, NY, USA, 2016, pp. 1-6, doi: 10.1109/LISAT.2016.7494106.
E. Batur and D. Maktav, "Assessment of Surface Water Quality by Using Satellite Images Fusion Based on PCA Method in the Lake Gala, Turkey," in IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 5, pp. 2983-2989, May 2019, doi: 10.1109/TGRS.2018.2879024.
Xuedi Zhang, Jianhua Wu and Baode Song, "Application of principal component analysis in groundwater quality assessment," 2011 International Symposium on Water Resource and Environmental Protection, Xi'an, 2011, pp. 2080-2083, doi: 10.1109/ISWREP.2011.5893671.
M. M. Than, K. M. Yee, K. Lint, M. Han and T. W. Hnin, "Determining Spatial and Temporal Changes of Water Quality in Hlaing River using Principal Component Analysis," 2020 IEEE Conference on Computer Applications(ICCA), Yangon, Myanmar, 2020, pp. 1-7, doi: 10.1109/ICCA49400.2020.9022841
M. Kumar, T. Singh, M. K. Maurya, A. Shivhare, A. Raut and P. K. Singh, "Quality Assessment and Monitoring of River Water Using IoT Infrastructure," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3238123.
R. K. Yadav, A. Jha and A. Choudhary, "IoT based prediction of water quality index for farm irrigation," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), Coimbatore, India, 2021, pp. 1443-1448, doi: 10.1109/ICAIS50930.2021.9395921.
G. Hassan, M. E. Shaheen and S. A. Taie, "Prediction Framework for Water Quality Parameters Monitoring via Remote Sensing," 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia, 2020, pp. 59-64, doi: 10.1109/SMART-TECH49988.2020.00029.
T. Cheng, F. Harrou, Y. Sun and T. Leiknes, "Monitoring Influent Measurements at Water Resource Recovery Facility Using Data-Driven Soft Sensor Approach," in IEEE Sensors Journal, vol. 19, no. 1, pp. 342-352, 1 Jan.1, 2019, doi: 10.1109/JSEN.2018.2875954.
“What Is Principal Component Analysis (PCA) and How It Is Used?” Sartorius, 18 Aug. 2020, [Online]. Available: www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186.
Singh, S. ., Bharti, A. K. ., Pandey, H. ., Yadav, R. K. ., Sharma, D. ., & Shanker, N. (2023). Towards Automated and Optimized Security Orchestration in Cloud SLA. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3), 215–222. https://doi.org/10.17762/ijritcc.v11i3.6339
Ms. Madhuri Zambre. (2012). Performance Analysis of Positive Lift LUO Converter . International Journal of New Practices in Management and Engineering, 1(01), 09 - 14. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/3
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