Leveraging Immersive Digital Twins and AI-Driven Decision Support Systems for Sustainable Water Reserves Management: A Conceptual Framework

Tianyu Zhao, Changji Song, Jun Yu, Lei Xing, Feng Xu, Wenhao Li and Zhenhua Wang ()
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Tianyu Zhao: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
Changji Song: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
Jun Yu: Yunhe (Henan) Information Technology Co., Ltd., Zhengzhou 450001, China
Lei Xing: Yunhe (Henan) Information Technology Co., Ltd., Zhengzhou 450001, China
Feng Xu: Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450001, China
Wenhao Li: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China
Zhenhua Wang: College of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi 832000, China

Sustainability, 2025, vol. 17, issue 8, 1-44

Abstract: Effective and sustainable water reserve management faces increasing challenges due to climate-induced variability, data fragmentation, and the limitations of traditional, static modeling systems. This study introduces a conceptual framework designed to address these challenges by integrating digital twins, IoT-driven real-time monitoring, game engine simulations, and AI-driven decision support systems (AI-DSS). The methodology involves constructing a digital twin ecosystem using IoT sensors, GIS layers, remote-sensing imagery, and game engines. This ecosystem simulates water dynamics and assesses policy interventions in real time. AI components, including machine-learning models and retrieval-augmented generation (RAG) chatbots, are embedded to synthesize real-time data into actionable insights. The framework enables the continuous assessment of hydrological dynamics, predictive risk analysis, and immersive, scenario-based decision-making to support long-term water sustainability. Simulated scenarios demonstrate accurate flood forecasting under variable rainfall intensities, early drought detection based on soil moisture and flow data, and real-time water-quality alerts. Digital elevation models from UAV photogrammetry enhance terrain realism, and AI models support dynamic predictions. Results show how the framework supports proactive mitigation planning, climate adaptation, and stakeholder communication in pursuit of resilient and sustainable water governance. By enabling early intervention, efficient resource allocation, and participatory decision-making, the proposed system fosters long-term, sustainable water security and environmental resilience. This conceptual framework suggests a pathway toward more transparent, data-informed, and resilient decision-making processes in water reserves management, particularly in regions facing climatic uncertainty and infrastructure limitations, aligning with global sustainability goals and adaptive water governance strategies.

Keywords: sustainability-centric metrics; smart water management; real-time analytics; interactive hydrological modeling; game engine; scenario planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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