Danial Golbaz
PhD Student
I am a PhD student in coastal and hydrologic modeling, with a background in civil and environmental engineering. My research focuses on compound flooding, nearshore wave dynamics, rainfall–runoff processes, and the interaction of surge, waves, tides, rainfall, and river discharge. I work with numerical models such as WRF-Hydro, FUNWAVE-TVD, SCHISM, and machine-learning surrogates to improve flood predictions. My goal is to connect physics-based modeling, data analysis, and practical coastal engineering applications for better assessment and decision-making.
Selected publications
- Amini, E., Nasiri, M., Pargoo, N. S., Mozhgani, Z., Golbaz, D., et al. (2023). Design optimization of ocean renewable energy converter using a combined Bi-level metaheuristic approach. Energy Conversion and Management: X, 19, 100371.
- Golbaz, D., Asadi, R., Amini, E., Mehdipour, H., et al. (2022). Layout and design optimization of ocean wave energy converters: A scoping review. Energy Reports, 8, 15446–15479.
- Amini, E., Mehdipour, H., Faraggiana, E., Golbaz, D., et al. (2022). Optimization of hydraulic power take-off system settings for point absorber wave energy converter. Renewable Energy, 194, 938–954.
- Amini, E., Asadi, R., Golbaz, D., Nasiri, M., et al. (2021). Comparative Study of Oscillating Surge Wave Energy Converter Performance: A Case Study for Southern Coasts of the Caspian Sea. Sustainability, 13(19), 10932.
- Amini, E., Golbaz, D., Asadi, R., Nasiri, M., et al. (2021). A Comparative Study of Metaheuristic Algorithms for Wave Energy Converter Power Take-Off Optimisation: A Case Study for Eastern Australia. Journal of Marine Science and Engineering, 9(5), 490.
- Amini, E., Golbaz, D., Amini, F., Nezhad, M. M., et al. (2020). A parametric study of wave energy converter layouts in real wave models. Energies, 13(22), 6095.
Education
- MS Coastal Engineering, University of Tehran, 2021
Currently working on
- Machine-Learning Surrogates for Hydrologic Prediction
CNN–LSTM surrogate models that emulate WRF-Hydro streamflow to accelerate scenario testing and explainable hydrologic analysis.
- Skew Surge and Extreme Sea-Level Analysis
Estimating extreme skew-surge probabilities from tide-gauge and atmospheric data, with nonstationarity detection and storm clustering.


