Post-doctoral Associate

Our research can be categorized into three major areas: 1) hydrological modeling and parameter estimation, 2) spatiotemporal variability of hydrological and meteorological variables, and 3) estimation and prediction of hydrological and meteorological variables using advanced techniques. In the first area, we address improvements in watershed, river, and reservoir routing models, particularly focusing on parameter estimation in ungauged basins, including methods for estimating peak discharge and design rainfall. The second area involves statistical and time-frequency analysis of hydrological and meteorological variables, with studies using wavelet analysis and probabilistic rainfall estimation. In the third area, we use innovative approaches such as computer vision and optical physics for rainfall estimation and aim to develop AI-based models for hydrological predictions, with recent achievements in hybrid models combining decomposition, feature selection, and AI for evapotranspiration prediction. Recently, we have focused our research on flood detection using synthetic aperture radar (SAR) data based on artificial intelligence. These advancements are expected to contribute significantly to agricultural water resource management and irrigation planning.