Classification of water quality based on aesthetic and chemical parameters
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Fengmin Cheng
Abstract
Consumers evaluate water quality mostly on the basis of its aesthetically acceptable qualities, which include flavor, odor, and appearance. The aesthetic acceptability of water is greatly impacted by the seven heavy metal characteristics: calcium, sodium, potassium, sulfate, magnesium, total hardness, and chloride. These parameters, in turn, have a profound effect on variables such as flavor and odor. The present paper attempts to enhance the level of prediction regarding water quality with the aid of machine learning algorithms. Accordingly, Extra Tree Classification (ETC) was applied to the projection of water quality parameters. In addition to this base model, the Subtraction-Average-Based Optimization and the Horned Lizard Optimization Algorithm were utilized to optimize its level of prediction accuracy. This strategic integration resulted in new hybrid model types. The ETC model, in combination with SABO, is the ETSA framework, which, with HLOA, integrates into the ETHL framework. During Testing for the Accuracy metric, the highest score was 0.986 from the ETHL model, while the ETSA model attained 0.972 and yielded the second best. In the Testing phase, concerning the Precision metric, the best performing model was, once again, the ETHL model with 0.986, while the ETC model developed the weakest performance with a value of 0.970. Future research could enhance water quality prediction through advanced hybrid models, real-time monitoring, deep learning, and validation across diverse water bodies and parameters.
Funding source: This work was supported by Hebei Province Key R&D Program Self Funded Project.
Acknowledgments
I 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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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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Author contributions: The author contributed to the study’s conception and design. Data collection, simulation and analysis were performed by “Fengmin Cheng”. Also the first draft of the manuscript was written by Fengmin Cheng commented on previous versions of the manuscript.
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Conflict of interest: The author declare no competing of interests.
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Research funding: This work was supported by Hebei Province Key R&D Program Self Funded Project.
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Data availability: The author do not have permissions to share data.
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