Artificial intelligence applications in water management: bibliometric analysis, challenges, and knowledge gaps

Abstract

Artificial intelligence (AI) has the potential to revolutionize water management (WM) by enabling accurate predictions and effective monitoring of water resources, especially in response to climate change (CC) challenges. This study offers insights into the current state of research on AI applications in water management (AI-WM) through bibliometric and systematic analyses. A total of 191 studies were selected following the identification, screening, and eligibility phases of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. The study's key findings include: (a) A bibliometric analysis identifying core concepts in AI-WM and underexplored aspects of WM, highlighting critical areas like water quality, consumption, and availability. (b) A classification of AI models and algorithms, emphasizing their application to specific WM areas and performance metrics used for evaluation. (c) An assessment of technical, data-related, socio-economic, ethical, and policy-related challenges in AI-WM, along with recommendations to address these challenges. (d) Identification of key research gaps and future research needs within AI-WM. Furthermore, this paper synthesizes challenges, knowledge gaps, and future research directions identified from over 25 review papers. The findings provide valuable guidance for researchers, policymakers, environmental agencies, utility companies, and communities seeking to improve WM through AI-driven solutions.