Research progress of the artificial intelligence application in wastewater treatment during 2012–2022: a bibliometric analysis
2023
Xiaoman Yu | Shuai Chen | Xiaojiao Zhang | Hongcheng Wu | Yaoguang Guo | Jie Guan
This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition. HIGHLIGHTS Using bibliometrics to explore the application of artificial intelligence in wastewater treatment.; The combination of deep learning has been developed.; Remote sensing technology has become a research hotspot.; Machine learning or deep learning modeling is widely used to build data-driven predictive models.;
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Este registro bibliográfico ha sido proporcionado por Directory of Open Access Journals