Development of Simulation System for Water Quality Accident using Deep Learning and Visualization Through Augmented Reality

With the advancement of industrialization, the number and quantity of hazardous chemical substances being released into the environment continue to increase. Currently, over 40,000 chemical substances are available for use in South Korea, with approximately 400 new chemicals imported and distributed annually. Among these, hazardous substances such as heavy metals and pesticides, when introduced into water systems through industrial activities, agricultural runoff, or accidental spills, pose significant risks to both environmental and human health. These substances are associated with various diseases, carcinogenic risks, and endocrine disruption, necessitating proactive management strategies. Existing water quality monitoring systems primarily function as reactive measures, focusing on incident detection rather than prevention. Although real-time monitoring methods can detect anomalies, they are limited in accurately predicting the transport pathways and concentration variations of hazardous chemicals. Moreover, environmental factors such as flow velocity, precipitation, and temperature significantly impact the dispersion process, which current monitoring approaches fail to adequately incorporate. To overcome these limitations, this study aims to develop a predictive system leveraging deep learning techniques for water pollution incident simulation and forecasting. This model integrates existing hydrodynamic and water quality models (e.g., EFDC, MIKE) with data-driven approaches to enhance predictive accuracy. Additionally, an augmented reality (AR)-based visualization system will be implemented to intuitively display pollutant dispersion and high-risk areas during water pollution incidents. AR devices such as HoloLens will be utilized to provide decision-makers, including environmental management agencies and local governments, with real-time analytical capabilities for rapid response. Furthermore, the system is designed to transition from reactive to preventive response strategies. By applying advanced algorithms, the system will automatically recommend priority response areas for emergency discharges and pollution containment measures. This study aims to enhance response capabilities to increasing water pollution incidents both domestically and internationally. By minimizing environmental and health impacts caused by hazardous chemicals, the proposed system is expected to contribute significantly to public safety. Furthermore, the integration of deep learning and augmented reality technologies represents a substantial advancement in environmental monitoring and predictive modeling.