Paper List

• 2020: Seamless integration of convolutional and back-propagation neural networks for regional multi-step-ahead PM2.5 forecasting
• 2022: Real-time image-based air quality estimation by deep learning neural networks
• 2022: Deep neural networks for spatiotemporal PM2.5 forecasts based on atmospheric chemical transport model output and monitoring data
• 2022: Integrate deep learning and physically-based models for multi-step-ahead microclimate forecasting
• 2023: Develop a hybrid machine learning model for promoting microbe biomass production
• 2023: High-spatiotemporal-resolution PM2.5 forecasting by hybrid deep learning models with ensembled massive heterogeneous monitoring data
• 2020: Interactive Urban Building Energy Modelling with Functional Mockup Interface of a Local Residential Building Stock
• 2024: Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models
• 2024: Advancing climate-resilient flood mitigation: Utilizing transformer-LSTM for water level forecasting at pumping stations
• 2020: Image Regression Classification of Air Quality by Convolutional Neural Network (seminar paper)
• 2023: A Vision of Agriculture 4.0: Constructing Smart Agriculture through Artificial Intelligent (seminar paper)
• 2022: A study on spatiotemporal groundwater level forecasting by a hybridization of machine learning and physically-based models (seminar paper)
• 2024: AI-Driven Hydro-Insights: Proactive Water Resource Management for Sustainable Agriculture in the Face of Climate Change (seminar paper)

More publications can be found at: ResearchGate Profile