Automating water quality analysis using ML and auto ML techniques

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@Article{venkata-vara-prasad:2021:Er,

• author = "D {Venkata Vara Prasad} and P {Senthil Kumar} and Lokeswari Y Venkataramana and G Prasannamedha and S Harshana and S {Jahnavi Srividya} and K Harrinei and Sravya Indraganti",
• title = "Automating water quality analysis using {ML} and auto {ML} techniques",
• journal = "Environmental research",
• year = "2021",
• volume = "202",
• pages = "111720",
• month = nov,
• keywords = "genetic algorithms, genetic programming, TPOT, Algorithms, Artificial Intelligence, Food Analysis, Humans, Machine Learning, Water Quality, AutoML, SMOTE, Water quality index",
• ISSN = "1096-0953",
• DOI = "doi:10.1016/j.envres.2021.111720",
• abstract = "Generation of unprocessed effluents, municipal refuse, factory wastes, junking of compostable and non-compostable effluents has hugely contaminated nature-provided water bodies like rivers, lakes and ponds. Therefore, there is a necessity to look into the water standards before the usage. This is a problem that can greatly benefit from Artificial Intelligence (AI). Traditional methods require human inspection and is time consuming. Automatic Machine Learning (AutoML) facilities supply machine learning with push of a button, or, on a minimum level, ensure to retain algorithm execution, data pipelines, and code, generally, are kept from sight and are anticipated to be the stepping stone for normalising AI. However, it is still a field under research. This work aims to recognize the areas where an AutoML system falls short or outperforms a traditional expert system built by data scientists. Keeping this as the motive, this work dives into the Machine Learning (ML) algorithms for comparing AutoML and an expert architecture built by the authors for Water Quality Assessment to evaluate the Water Quality Index, which gives the general water quality, and the Water Quality Class, a term classified on the basis of the Water Quality Index. The results prove that the accuracy of AutoML and TPOT was 1.4 percent higher than conventional ML techniques for binary class water data. For Multi class water data, AutoML was 0.5 percent higher and TPOT was 0.6percent higher than conventional ML techniques.",
• notes = "PMID: 34297938",

}

Genetic Programming entries for D Venkata Vara Prasad P Senthil Kumar Lokeswari Y Venkataramana G Prasannamedha S Harshana S Jahnavi Srividya K Harrinei Sravya Indraganti

Citations