AI-Supported Reality: Revisiting Models and Techniques of Systems Analysis in Water Resources and Agriculture Management

This paper reviews contemporary developments in systems analysis applied to water resources and agricultural management, highlighting the growing influence of artificial intelligence (AI) and machine learning (ML). The literature in this field encompasses a wide range of approaches, methods, and applications, including hydrological simulation models, decision-support systems, and participatory governance frameworks. In recent years, increasing attention has been devoted to systematically reviewing and categorizing these approaches, particularly in light of rapid advances in AI- and ML-based technologies. The present study focuses on the contributions and impacts of AI and ML on systems analysis methodologies compared with the state of the field approximately a decade ago. By revisiting and classifying key groups of approaches, methods, and software tools, the paper provides an updated overview of the current status of systems analysis in water resources and irrigation management. This overview also serves as a reference framework for assessing future methodological and technological developments. Adopting a systems-thinking perspective, the review spans multiple spatial and management scales, from plot-level irrigation practices to river-basin water allocation. The paper aims to support a more holistic understanding and improved design and evaluation of water–agriculture systems, while also strengthening policy support for sustainable resource management. Finally, it highlights the need for continued interdisciplinary integration, enhanced stakeholder participation, and the development of operational tools capable of translating complex systems insights into actionable water management strategies in the emerging context shaped by AI and ML.