A5030054277- Hydrological Forecasting Using AI
- Hydrology and Watershed Management Studies
- Flood Risk Assessment and Management
- Water Quality Monitoring Technologies
- Groundwater flow and contamination studies
- Precipitation Measurement and Analysis
- Anaerobic Digestion and Biogas Production
- Water Quality Monitoring and Analysis
- Advanced Photocatalysis Techniques
- Cryospheric studies and observations
- MXene and MAX Phase Materials
- Computational Physics and Python Applications
- Phosphorus and nutrient management
- Smart Grid Energy Management
- Water resources management and optimization
- Electrocatalysts for Energy Conversion
- Environmental DNA in Biodiversity Studies
- Ultrasound and Cavitation Phenomena
- Soil and Water Nutrient Dynamics
- Ammonia Synthesis and Nitrogen Reduction
- Scientific and Engineering Research Topics
- Adsorption and biosorption for pollutant removal
- Machine Learning in Materials Science
- Advanced Image Fusion Techniques
- Geophysical and Geoelectrical Methods
King Abdullah University of Science and Technology
2023-2024
Ulsan National Institute of Science and Technology
2020-2023
Carbon-based transition metal (TM) single-atom catalysts (SACs) have shown great potential toward electrochemical water splitting and H 2 production.
Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse seasonal changes. Deep learning models have demonstrated predictive power water due to the superior ability automatically learn complex patterns relationships from variables. Long short-term memory (LSTM), one deep for prediction, type recurrent neural network that can account longer-term traits time-dependent data. It most widely applied used predict time...
Abstract. Machine learning has shown great promise for simulating hydrological phenomena. However, the development of machine-learning-based models requires advanced skills from diverse fields, such as programming and modeling. Additionally, data pre-processing post-processing when training testing machine are a time-intensive process. In this study, we developed python-based framework that simplifies process building automates model results. Pre-processing utilities assist in incorporating...
Precisely measuring the adsorption capability of materials towards toxic heavy metal ions in aqueous solution is essential for synthesis effective novel adsorbents.
Abstract. Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable reliable dataset remains challenge for users. We conducted comprehensive global evaluation date (sub-)daily P using hydrological modeling. A total 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate performance, we calibrated conceptual model HBV against observed daily...
In recent years, there has been a significant increase in the release of datasets across various domains, including water resources. This surge is driven by advancements computational and storage technologies, as well growing need to develop robust, accurate data-driven solutions address challenges such climate change, scarcity, environmental pollution. As result, wealth national global spatio-temporal become freely accessible online. These are invaluable for applications like flood...
Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable reliable dataset remains challenge for users. We conducted comprehensive global evaluation date (sub-)daily $P$ using hydrological modeling. A total 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate performance, we calibrated conceptual model HBV against observed daily streamflow...
Inland water frequently occurs during harmful algal blooms (HABs), rendering it challenging to comprehend the spatiotemporal features of dynamics. Recently, remote sensing has been applied effectively detect behaviors in expensive bodies. However, image sensor resolution limitation can render understanding relatively small bodies challenging. In addition, few studies have improved images investigate inland quality, owing limitations. Therefore, this study deep learning-based Super-resolution...