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

The study frames intelligent knowledge management in water treatment through four lines of analysis: thematic trends, collaboration networks, research gaps, and future research priorities. Relative to single-technology reviews, it places thematic evolution, collaboration structure, and gap identification within one analytical frame, aiming at a field-level picture rather than one technical route. Based on the title and reference structure, the analysis rests on bibliometric and scientometric methods; because the loaded text is incomplete, the specific data sources, search strategy, and sample size are not presented in the text.

The references cover multiple AI and machine-learning applications in water treatment, including groundwater quality assessment, drinking water turbidity prediction, water quality index prediction, and AI in wastewater treatment. These entries move intelligent knowledge management from a conceptual level to concrete water treatment tasks, indicating the field has extended from method exploration to water quality prediction and process management. The text lists verifiable records such as Emami and Choopan on AI methods for groundwater quality assessment, Khalatbari and Dagbandan on GMDH neural networks for drinking water turbidity prediction, and Han et al. on machine-learning prediction of the water quality index.

The references also include organization-level knowledge management studies, such as knowledge management model design, knowledge reuse cases, and the effect of knowledge management on organizational performance. This suggests intelligent knowledge management in water treatment is not only an algorithmic matter but also involves organizational knowledge processes and governance arrangements. The text lists Akfian and Fakhur Thaqiyah on designing a knowledge management model in a water and wastewater company, Lillemoen and Falk on knowledge reuse in a small water treatment company, and Rezaei et al. on knowledge management and organizational performance.

The references include several bibliometric and scientometric reviews of water and wastewater treatment, forming the methodological reference base for this study. These reviews provide comparable precedents for identifying thematic trends and research gaps, positioning the study within an existing tradition of metric reviews. The text lists Jayapriya's scientometric analysis of water treatment from 2011 to 2020, Valdiviezo Gonzales et al. on drinking water treatment technologies, Li et al. on AI in wastewater treatment, and Zhou et al. on machine learning in wastewater treatment.

The study addresses researchers in water treatment, knowledge managers in water utilities, and research planners, and applies to literature-review settings where the thematic distribution and collaboration structure of intelligent knowledge management need to be understood; its analytical frame rests on scientometric methods, so conclusions apply to literature-level trend identification rather than direct guidance on process operating parameters.

Because the loaded text is incomplete, the databases searched, time span, number of documents, and specific metrics are not presented, so the strength of the thematic trend and collaboration network conclusions cannot be judged; readers should still watch how the study defines the boundary of intelligent knowledge management and on what basis research gaps and future priorities are proposed.