Ulsan National Institute of Science and Technology · 環境科学
Professor Sang-Soo Baek's research lab specializes in environmental data science and advanced materials for sustainable water and energy systems. The lab focuses on developing deep learning and machine learning models to predict water quality, algal blooms, and dissolved organic matter dynamics in river systems, integrating hydrological, meteorological, and water quality data. Concurrently, the lab designs and optimizes novel metal–organic frameworks (MOFs) and carbon-based nanocomposites for high-performance energy storage applications, particularly supercapacitors. The interdisciplinary approach bridges environmental monitoring with materials innovation to address global challenges in water security and clean energy.
Figures are computed from collected data and may differ slightly.
A Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) combined with a deep learning approach was created by combining CNN and LSTM networks simulated water quality including total nitrogen, total phosphorous, and total organic carbon. Water level and water quality data in the Nakdong river basin were collected from the Water Resources Management Information System (WAMIS) and the Real-Time Water Quality Information, respectively. The rainfall radar image and operation information of
In several countries, the public health and fishery industries have suffered from harmful algal blooms (HABs) that have escalated to become a global issue. Though computational modeling offers an effective means to understand and mitigate the adverse effects of HABs, it is challenging to design models that adequately reflect the complexity of HAB dynamics. This paper presents a method involving the application of deep learning to an ocean model for simulating blooms of Alexandrium catenella . Th
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