Selected projects spanning hydrological modeling, machine learning, and field data platforms.
Physics-Informed Deep Learning for Multi-Basin River Discharge Forecasting
Integrating process-based hydrology with deep learning for operational forecasting
Developed a multi-basin river discharge forecasting model that combines physics-informed machine learning with a differentiable hydrology core. The architecture integrates Mamba state-space layers for temporal dynamics, causal graph neural network routing for inter-basin flow propagation, and a differentiable hydrology module to preserve physical consistency across basins.
Computational Groundwater Flow Modeling
MSc research — Python-based groundwater simulation
For my master’s research at the University of Zanjan, I developed groundwater flow models using Python. The work applied numerical methods to simulate subsurface flow and support analysis of hydraulic structures and groundwater systems.
eOceans Mobile App
Data collection platform for oceanographic research
I contributed to the development of eOceans, a mobile data collection platform that helps oceanographers and research teams capture and organize field observations. The app supports real-time environmental data collection in the field, enabling faster and more collaborative marine research.
The platform has been featured in peer-reviewed scientific publications and has informed international policies to protect endangered species and special marine areas.