METHODS AND PRACTICES FOR IMPLEMENTING AND APPLYING DIFFERENT TYPES OF ARTIFICIAL INTELLIGENCE AND DATA SCIENCE TO SOLVE PROBLEMS IN WATER INFRASTRUCTURE
DOI:
https://doi.org/10.31650/2786-6696-2024-9-97-104Keywords:
artificial intelligence, data science, water quality, leakage detection, water flow control.Abstract
The subject of the study in this article was the practice of implementing and applying various types of artificial intelligence and data science to detect leaks from water supply networks, to monitor freshwater conditions and to detect pollution, clean freshwater bodies from waste from industrial and mining enterprises, control freshwater flow and develop more efficient water filtration methods.
The article identifies the advantages and disadvantages of the practice of introducing and applying various types of artificial intelligence and data science technologies to automate traditional methods of monitoring, control and related work in the water industry. The article addresses the following objectives: to substantiate the effectiveness of implementation of various types of artificial intelligence technologies and data science methods and their practical application with software and hardware technologies to automate traditional methods of performing work in the water industry.
To solve the tasks set, the methodology used was based on general scientific and special research methods, such as theoretical methods (analysis, explanation, generalisation, comparison).
The use of this approach allowed us to obtain the following results: the features that affect the accuracy of the analysis of collected data used by technologies of various types of artificial intelligence and data science methods were identified. Practices and methods for more efficient and accurate application of this technology are reflected.
Scientific data are analysed. The study allowed identifying the practical opportunities and problems of this technology.
Recommendations for the effective use of this technology have been developed. The factors that influence the effective use of this technology in industry are identified.
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