Fundamental feature-based assessment of water quality through optimal enhanced models
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Peng Xu
and
Jidong Zhang
Abstract
High water quality (WQ) is of the essence to human health and environmental sustainability. The resultant effects are viewed in industrial and agricultural productivity and the drinking water supply. In light of the recent trend of water-based tourism and recreational activities, it is now more than ever relevant to commit to stringent enforcement of the regulations on WQ. Compliance with these norms will ensure that the security of drinking water, agricultural productivity, and industrial processes is maintained accordingly. It can predict water quality by finding patterns within different water parameters by analyzing past and present data. The given study explains the use of Support Vector Classification (SVC), Gradient Boost Classification (GBC), and Random Forest Classification (RFC) that have been optimized using Fitness Dependent Optimizer Algorithm (FDOA) and Cheetah Optimization Algorithm (COA), respectively, to improve their accuracy in projection. Considering the results of evaluation metrics for models, the GBCO, SVCO, and RFCO models registered 0.992, 0.966, and 0.944 values of Accuracy, while the GBC, SVC, and RFC single models achieved 0.956, 0.933, and 0.904 values. Implementing the optimization algorithms, especially the COA, enhanced the Accuracy of single models as 3.6 %, 3.3 %, and 4 % for GBC, SVC, and RFC.
Acknowledgments
We would like to take this opportunity to acknowledge that there are no individuals or organizations that require acknowledgment for their contributions to this work.
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Research ethics: Research involving Human Participants and Animals: The observational study conducted on medical staff needs no ethical code. Therefore, the above study was not required to acquire ethical code.
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Informed consent: This option is not neccessary due to that the data were collected from the references.
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Author contributions: All authors contributed to the study’s conception and design. Data collection, simulation and analysis were performed by” Jidong ZHANG and Peng XU”. Also, the first draft of the manuscript was written by Jidong ZHANG. Peng XU commented on previous versions of the manuscript.
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Use of Large Language Models, AI and Machine Learning Tools: During the preparation of this work, the authors used Large Language Models, AI, and Machine Learning tools for tasks such as language refinement, data analysis, or figure generation, with all outputs being reviewed and validated by the authors to ensure accuracy and originality. After using these tools/services, the authors reviewed and edited the content and take full responsibility for the content of the published article.
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Conflict of interest: The authors declare no competing of interests.
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Research funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
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Data availability: The authors do not have permissions to share data.
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