Integrating knowledge management practices in higher education with sustainable smart hydrological ecosystems: an AI-driven approach to green innovation

ABSTRACT This study investigates the dual impact of knowledge management (KM) practices within higher education and their application in sustainable smart hydrological ecosystems, aimed at fostering green innovation. By leveraging data collected from university students and faculty regarding KM practices, alongside environmental data from hydrological sensors and monitoring systems, this research bridges educational insights with environmental sustainability. The study utilizes radial basis function networks to analyze complex, multi-source data, including real-time environmental parameters and knowledge dissemination metrics within educational institutions. The findings reveal how effective KM practices within higher education can influence broader environmental sustainability outcomes, particularly in enhancing the resilience of hydrological ecosystems. This integrated approach underscores the potential of AI-driven KM systems to contribute significantly to both educational excellence and global green innovation efforts.

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Publication Details

Journal
Hydrology research
Published
2025-06-24
DOI
https://doi.org/10.2166/nh.2025.147
Citations
1
Primary Topic
Engineering Education and Technology
Type
article
Field-Weighted Citation Impact
3.25
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article

Integrating knowledge management practices in higher education with sustainable smart hydrological ecosystems: an AI-driven approach to green innovation

Arif Sarı, Feyza Bhatti, Berna Gurler
1 citations
Hydrology research
Engineering Education and Technology
3.25
article

Integrating knowledge management practices in higher education with sustainable smart hydrological ecosystems: an AI-driven approach to green innovation

Arif Sarı, Feyza Bhatti, Berna Gurler
article en
1 citations

Abstract

ABSTRACT This study investigates the dual impact of knowledge management (KM) practices within higher education and their application in sustainable smart hydrological ecosystems, aimed at fostering green innovation. By leveraging data collected from university students and faculty regarding KM practices, alongside environmental data from hydrological sensors and monitoring systems, this research bridges educational insights with environmental sustainability. The study utilizes radial basis function networks to analyze complex, multi-source data, including real-time environmental parameters and knowledge dissemination metrics within educational institutions. The findings reveal how effective KM practices within higher education can influence broader environmental sustainability outcomes, particularly in enhancing the resilience of hydrological ecosystems. This integrated approach underscores the potential of AI-driven KM systems to contribute significantly to both educational excellence and global green innovation efforts.

Hydrology researchVol. 56(9)
Girne American University (CY)
Openalex Percentile: Top 8%
Engineering Education and Technology
3.25
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