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Mehrdad MohannazadehView profile

Researcher

• Hydrological Modelling
• Machine Learning
• Applied Mathematics
• +3 more

Mehrdad Mohannazadeh is a postdoctoral researcher at the Department of Computational Hydrosystems (CHS) within the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on hydrological modelling , machine learning , and applied mathematics , particularly for analyzing and improving flood prediction models. Education : Ph.D. in Computer Sciences (2019-2023), Bielefeld University M.Sc. in Applied Mathematics (2016-2018), University of Applied Sciences Mittweida B.E. in Electrical Engineering (Electronics) (2007-2012), Shahid Beheshti University His research involves modifying hydrological models for better forecasting, working with projects like MOSES , HI-CAM II , 4DHydro , and SCENIC . His publications emphasize interpretable neural networks , probabilistic classification , and climate change impacts on water systems. Recent work includes high-resolution drought monitoring in Germany and seasonal hydrological forecasting for Andean-Amazonian basins. He contributes to UFZ Young Scientist Award winning initiatives and European forest model evaluations.

• Machine Learning
• Regression Analysis
• Artificial Intelligence
• +7 more

Marcin Blachnik is a Professor at the Department of Industrial Informatics within the Faculty of Materials Engineering at Silesian University of Technology. His research focuses on information and communication technology, particularly in machine learning, computational intelligence, and data processing methodologies. Specializes in regression, ensemble methods, and prototype-based clustering Active in optimization of electric arc furnace processes Contributions to artificial intelligence applications in industrial systems The tag cloud from his publications highlights keywords like "machine learning," "regression," "artificial intelligence," and "LVQ." Marcin Blachnik has documented outputs including 80 publications, with an h-index of 13 (Scopus) and 12 (WoS), accumulating a total impact factor of 48.321 and a ministerial score of 2,165.