GoPubby - Machine Learning at the Intersection of Artificial Intelligence and Data Mining

Artificial Intelligence (AI), Machine Learning (ML), and Data Mining (DM) are very fancy words nowadays in technology fields. But do you know the differences between these terms? They are all dedicated to the transformation of data to usable knowledge and support decisions. In other words, almost all disciplines will need them. AI is a broad term for using either data or knowledge to offer solutions to existing problems that require some search or reasoning. ML is a specific subarea of AI that offers procedures learning from data. DM, is the an interdisciplinary field to discover patterns in large data sets and subsumes both ML, statistical modeling, or visualization disciplines. The following figure gives you an idea about the relationship of the three terminologies, and the bracketed numbers in the figure denote the number of papers dealing with these technologies in the water and wastewater sectors, respectively, according to Hadjimichael et al. in a paper published in 2019.

With regard to the specific methods within the water and wastewater fields, artificial neural network is the most popular method followed by clustering. There are huge potentials for further exploration, as the encapsulation of ML into functional decision support system is not fully investigated. This is because: (1) there is a lack of an association between the fields of water engineering and computer engineering; (2) most ML techniques were adopted by academics with limited practical experience; (3) ML techniques are inherently multi-disciplinary and combined with problems of great complexity facing water and wastewater systems; and (4) there is a lack of user-friendly interfaces and support after product delivery. With that said, I'm working on an integration of machine learning into my discipline of water and wastewater treatment. Sound promising?

Machine Learning at the Intersection of Artificial Intelligence and Data Mining

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