A Quantum-Hybrid Framework for Urban Environmental Forecasting Integrating Advanced AI and Geospatial Simulation.

Area of Science:

• Environmental Science
• Computer Science
• Quantum Computing

Background:

• Environmental monitoring faces challenges with forecast accuracy and processing complex, small datasets.
• Current modeling technologies struggle with the high discretization of environmental data.

Purpose of the Study:

• To develop advanced forecasting and modeling technologies for environmental processes.
• To enhance the accuracy of environmental parameter forecasting using hybrid classical and quantum methods.
• To address key problems in environmental monitoring, including data complexity and prediction reliability.

Main Methods:

• Integration of deep neural networks (e.g., LSTM, Transformer) and classical algorithms (AutoARIMA, BATS) with quantum computing approaches.
• Development of hybrid forecasting models incorporating quantum layers.
• Application of spatial interpolation techniques and the A* algorithm with a Gaussian kernel for an air pollution simulator.

Main Results:

• Experimental validation confirmed the effectiveness of the proposed hybrid classical and quantum forecasting methods.
• Demonstrated improved accuracy in predicting environmental parameters compared to traditional approaches.
• Successfully developed an adaptive system concept for predicting environmental conditions in urban areas.

Conclusions:

• The proposed hybrid models offer a significant advancement in environmental forecasting accuracy and adaptability.
• Developed models are practical for operational monitoring and forecasting of environmental threats.
• Results are applicable to environmental information systems for enhanced predictive capabilities.