Intelligent Knowledge Management in Water Treatment: A Scientometric Analysis of Thematic Trends, Collaboration Networks, Research Gaps, and Future Research Priorities

Purpose: Knowledge management (KM) has been recognized as a critical strategic framework that enables organizations to effectively leverage intellectual assets and improve operational efficiency. In the water treatment industry, KM encompasses the collection, organization, sharing, and utilization of employees’ technical knowledge and specialized expertise. This process fosters technological innovation, enhances system productivity, and improves service quality.
In recent years, the role of KM in organizations, including the water sector, has entered a new phase, becoming closely integrated with artificial intelligence (AI). This shift has attracted considerable scholarly attention, resulting in a growing body of research outputs. One of the most effective ways to evaluate these outputs is through scientometric analysis. Accordingly, this study investigates scientific production in the domain of intelligent KM in water treatment, with the aim of identifying thematic trends, collaboration networks, research gaps, and future research priorities.

Methodology: This applied research employs scientometric techniques to analyze scientific publications related to intelligent KM in water purification. Relevant articles were retrieved from the Scopus and Web of Science databases. From an initial pool of 172 documents, 49 were selected after screening titles, abstracts, and keywords. Scientometric analyses were conducted using the bibliometrix package in R. To address the research questions, bibliometric techniques and word co-occurrence networks were applied. Frequent keywords were identified, dominant conceptual clusters were mapped, and influential thematic trends were examined. Strategic diagrams, thematic mapping, and hierarchical clustering were further utilized to construct a comprehensive science map of the field.

Findings: Since 2015, there has been an increasing focus on knowledge management in this field. In 2024, the highest number of documents were published, with 8 scientific productions. Other notable years include 2022 and 2018, with 7 and 5 articles published respectively, showcasing a growing trend. China, the United States, and the United Kingdom were the most active countries with 11, 10, and 6 scientific documents respectively. Beijing University of Technology and Tianjin University have played crucial roles in generating knowledge and advancing new technologies in this field. The source IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, with 3 articles, is ranked first among sources publishing documents, with most sources being Q1. The top researchers, Han Honggui and Qiao Junfei, each have 4 articles, while Wu Xiaolong has 3 articles, demonstrating their significant contributions to research knowledge production. Vocabulary analysis and topic trends indicate that artificial intelligence, machine learning, environmental monitoring, and intelligent systems are the most frequently discussed topics.
The trend chart shows a progression in topics within this field, starting from decision support systems and water purification, transitioning to water management and decision-making supported by knowledge management systems, and finally focusing on artificial intelligence and system training through machine learning. Hierarchical clustering has identified three thematic axes: water resources management and safe water supply through new technologies, the emergence of new technologies and support systems, and water quality monitoring through system training. Strategic topics such as wastewater treatment, data mining, decision support systems, and knowledge management are considered mature topics that will shape future research directions. Additionally, topics like denitrification, nitrogen removal, and freshwater resource management are fundamental to supporting the main research axes. Climate change, cost-effectiveness, ecosystems, and water pollution are highlighted as emerging or declining topics. Quality control, environmental monitoring, and the aquatic environment are positioned centrally in the diagram, acting as bridges between the core topics and the driving forces in the field.
Conclusion: The findings indicate that the field of intelligent KM in water treatment has achieved relative stability in recent years and continues to maintain a strong position within interdisciplinary research. Analysis shows that core topics—including KM, data mining, intelligent organizations, and advanced water treatment technologies—remain central to scholarly attention and play a crucial role in optimizing treatment processes. Emerging trends suggest a multidimensional evolution, with the convergence of KM and advanced technologies offering pathways to enhance water quality, promote environmental sustainability, and reduce costs, time, and energy consumption. Strengthening international collaboration, expanding scientific networks, and adopting AI-, machine learning–, and data mining–based approaches can further consolidate and advance the field. In summary, the future of research related to knowledge management in water purification will depend on the use of smart technologies, extensive global interactions, holding global conferences in this field, and effective participation in international scientific cooperation.