Artificial Intelligence-Based Decision Support Systems for Enhancing Administrative Efficiency in Drinking Water Management

This study examines the role of Artificial Intelligence (Al)-based Decision Support Systems (DSS) in enhancing administrative efficiency in drinking water management. Water utilities are increasingly challenged by regulatory complexity, climate change, resource scarcity, and rising demand, which require more integrated, data-driven, and adaptive management approaches. Traditional administrative systems often lack the capability to process large-scale, heterogeneous data and to support timely and informed decision-making. The paper proposes a conceptual framework for AI-based DSS that integrates data infrastructure, advanced analytical methods, and human-AI collaboration within a governance-oriented approach. Machine learning models, such as Random Forest algorithms, enable predictive analytics for water flow, pressure, and infrastructure performance, supporting operational planning, maintenance, and leakage detection. Furthermore, the study highlights the importance of interoperability, data quality, and multi-stakeholder coordination in ensuring effective system implementation. Key administrative functions are significantly improved through AI-driven insights. However, the study also emphasizes challenges related to ethical risks, transparency, data governance, and organizational change. Ultimately, AI-based DSS is presented as a transformative tool for achieving efficient, resilient, and sustainable drinking water management systems.