Ataollah Shirzadi | Natural Hazards | Best Researcher Award

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Dr. Ataollah Shirzadi | Natural Hazards | Best Researcher Award

University of Kurdistan, Iran.

Dr. Ataollah Shirzadi is an Assistant Professor at the University of Kurdistan, Iran, specializing in Watershed Management Engineering. With over 90 peer-reviewed publications, he has become an internationally recognized figure in the field of natural hazard modeling. His expertise spans flash flood forecasting, shallow landslide prediction, soil erosion modeling, and geospatial risk assessment, all enhanced through artificial intelligence techniques. Notably, he was listed among the top 2% and 1% of global scientists in 2022 and 2023 by Stanford University and ISC. Dr. Shirzadi has contributed to several high-impact collaborative projects, including an international Iran-China initiative on flood susceptibility. He serves as a reviewer and editorial member for multiple international journals and actively mentors graduate researchers. His research bridges theoretical modeling and real-world disaster management, making significant strides in environmental resilience.

🔎Author Profile

🏅Awards and Honors

  1. Ranked among the Top 2% Scientists in the World

    • Source: Stanford University Ranking

    • Years: 2022 and 2023

  2. Ranked among the Top 1% Scientists in the Islamic World

    • Source: ISC (Islamic World Science Citation Center)

    • Year: 2023

  3. Elite and Talent Recognition Awards

    • By: Iran’s National Elite Foundation (for undergraduate and postgraduate academic excellence)

    • Context: Received for high GPA and top-ranking achievements during B.Sc. and M.Sc. studies

  4. National University Admission Ranks

    • Ranked 1st in M.Sc. entrance exam for the field of Mass Movements in Watershed Management

    • Ranked 3rd in B.Sc. entrance exam for Hydrology and Climatology

  5. Excellence Awards

    • University Honors: Recognized multiple times as top student in academic performance at both undergraduate and postgraduate levels

🏆Strengths for the Award

  1. High-Impact Research Contributions
    Dr. Shirzadi has co-authored over 90 international publications, with many articles appearing in top-tier journals such as Science of The Total Environment, Journal of Hydrology, Environmental Modelling & Software, Geomatics, Natural Hazards and Risk, and Remote Sensing. His research has garnered significant citations (e.g., 756 citations for a 2018 paper and 699 for a 2019 paper), reflecting the impact and relevance of his work in the scientific community.

  2. Pioneering Work in Natural Hazard Modeling
    His expertise lies in landslide, flash flood, and flood susceptibility modeling, with a strong emphasis on machine learning, deep learning, and hybrid AI techniques. Notably, he has developed several novel hybrid intelligence models integrating techniques like ANFIS, Genetic Algorithms, SVMs, Decision Trees, and ensemble approaches, widely cited and validated across diverse geographies.

  3. Recognition & Global Ranking
    He has been recognized among the top 2% and 1% of scientists globally by Stanford University and the Islamic World Science Citation Center (ISC) in 2022 and 2023 — a rare distinction that underlines his international academic standing.

  4. Editorial and Reviewer Excellence
    He holds editorial roles in journals such as Frontiers in Earth Science and has reviewed for more than 30 ISI-indexed journals, including Scientific Reports, Journal of Hydrology, CATENA, and Environmental Earth Sciences. This affirms his reputation as a trusted evaluator of cutting-edge scientific work.

  5. Academic Mentorship and Leadership
    Dr. Shirzadi has supervised over 17 M.Sc. and Ph.D. students, contributing actively to capacity building in environmental modeling and risk assessment. He has also been involved in international collaborations, including a recent Iran-China flood monitoring project, reflecting both leadership and global outreach.

🎓 Education 

Dr. Shirzadi earned his Ph.D. in Watershed Management Engineering (2014–2017) from Sari Agricultural Sciences and Natural Resources University, where his thesis focused on spatial prediction of shallow landslides using advanced data mining algorithms. He previously obtained an M.Sc. in Mass Movements (2007–2009) with a GPA of 19/20, also from Sari University, where he developed regional models for rockfall hazard mapping. His B.Sc. in Hydrology and Climatology (2003–2007) was awarded by the University of Agricultural Science and Natural Resources of Gorgan . Over the years, Dr. Shirzadi has cultivated robust expertise in geospatial modeling, sediment transport, and flood risk analysis. He also holds certifications in GIS, SPSS, AutoCAD, and multiple machine learning platforms, demonstrating both strong academic credentials and technical fluency.

🔬 Research Focus on Natural Hazards

Dr. Shirzadi’s research centers on the integration of AI, machine learning, and geospatial analysis to assess and mitigate natural hazards. His core focus includes:

  • Flood Susceptibility Mapping using hybrid machine learning models (e.g., ANFIS, rotation forests).

  • Shallow Landslide Prediction with ensemble algorithms combining decision trees, support vector machines, and neural networks.

  • Risk Zonation for erosion and sediment transport based on satellite data and multi-criteria analysis.

  • Uncertainty Quantification in hazard models to improve resilience strategies.

  • Remote Sensing Applications using Sentinel-1/2 and Landsat data for urban and rural hazard detection.

His goal is to create smart, scalable, and interpretable models that guide land use planning, policy-making, and emergency preparedness in climate-sensitive regions.

📘 Publication Top Notes

  1. A Comparative Assessment of Decision Trees Algorithms for Flash Flood Susceptibility Modeling
    Authors: Khosravi K., Pham B.T., Chapi K., Shirzadi A., et al.
    Journal: Science of The Total Environment (2018)
    Summary: Compared multiple decision tree algorithms to model flash flood susceptibility in Iran’s Haraz watershed, showing ensemble methods outperformed traditional single classifiers.

  2. Flood Susceptibility Modeling Using MCDM and Machine Learning
    Authors: Khosravi K., Shahabi H., Adamowski J., Shirzadi A., et al.
    Journal: Journal of Hydrology (2019)
    Summary: Evaluated and contrasted MCDM techniques and AI models; hybrid ML models proved superior in spatial flood risk analysis.

  3. A Novel Hybrid AI Approach for Flood Susceptibility
    Authors: Chapi K., Singh V.P., Shirzadi A., et al.
    Journal: Environmental Modelling & Software (2017)
    Summary: Developed a hybrid model combining several AI algorithms to predict flood risk, improving classification accuracy in complex terrains.

  4. Flood Susceptibility Using ANFIS-GA-DE Hybrid
    Authors: Hong H., Panahi M., Shirzadi A., et al.
    Journal: Science of the Total Environment (2018)
    Summary: Applied ANFIS enhanced with genetic and differential evolution algorithms for flood prediction in China’s Hengfeng area.

  1. Forecasting Floods Using ML & Statistical Models
    Authors: Shafizadeh-Moghadam H., Shahabi H., Shirzadi A., et al.
    Journal: Journal of Environmental Management (2018)
    Summary: Combined ML and traditional statistical techniques to forecast flood-prone zones, optimizing accuracy and runtime efficiency.

🚀Conclusion

Dr. Ataollah Shirzadi stands out as an exceptionally qualified candidate for the Best Researcher Award, with an impressive combination of scholarly output, innovative AI-based methodologies, global recognition, and academic service. His work not only advances theoretical models but also addresses urgent environmental and societal challenges. With continued growth in communication and cross-disciplinary application, he is poised to make even greater contributions to science and practice.

Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

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Prof. Dr Yoshitada Morikawa | Quantum Simulations | Best Researcher Award

Professor, The University of Osaka, Japan.

Professor Yoshitada Morikawa is a leading Japanese physicist and materials scientist specializing in quantum simulations. Born in Osaka in 1966, he currently serves as a Professor in the Department of Precision Engineering at Osaka University. With a rich academic journey spanning Kyoto University and the University of Tokyo, he has significantly contributed to computational physics, surface science, and AI-driven materials design. Professor Morikawa is known for combining quantum mechanics with machine learning to explore and optimize surface/interface phenomena, catalysis, and semiconductor behavior. His scholarly work includes over 218 peer-reviewed publications and a remarkable h-index of 49. His impact is further demonstrated through leadership roles in the Japan Society of Vacuum and Surface Science and the Physical Society of Japan. Widely respected for his visionary research and scientific leadership, Professor Morikawa is a strong advocate for a carbon-neutral society through fundamental science.

  📌Author’s Profile

🎓 Education 

Yoshitada Morikawa received his B.Sc. in Physics and Chemistry in 1989 and M.Sc. in Chemistry in 1991, both from Kyoto University. He then earned his Ph.D. in Physics in 1994 from the Institute for Solid State Physics, University of Tokyo. His education laid a robust foundation in theoretical and computational science, equipping him with the necessary tools to explore the intersections of quantum mechanics, chemistry, and material interfaces. During his doctoral studies, he held a prestigious Japan Society for the Promotion of Science (JSPS) Fellowship (DC), followed by a postdoctoral fellowship (PD) at Kyoto University. These early roles catalyzed his deep involvement in atomic-scale material analysis and first-principles simulations. Professor Morikawa’s academic path exemplifies a seamless integration of multi-disciplinary domains and a commitment to scientific rigor, establishing him as a globally recognized figure in quantum materials research and theory-driven computational modeling.

🧪 Experience 

Professor Morikawa’s career spans over three decades of distinguished service in academic and national research institutions. After his Ph.D., he joined the Joint Research Center for Atom Technology (JRCAT) and later served at the National Institute of Advanced Industrial Science and Technology (AIST). He held visiting positions at JAIST and the Technical University of Denmark. Since 2004, he has been with Osaka University, first as an Associate Professor at ISIR and then, from 2009, as a full Professor in the Graduate School of Engineering. He has supervised major projects involving surface physics, electrochemistry, and materials simulations. His leadership roles include serving as Vice President of the Japan Society of Vacuum and Surface Science and Representative of the Physical Society of Japan’s Division 9. Professor Morikawa’s vast experience in academic, industrial, and international contexts makes him a valuable leader and a mentor in materials science innovation.

🔬 Research Focus

Professor Morikawa’s research explores quantum mechanical simulations of surfaces and interfaces, targeting real-world problems in energy, catalysis, and semiconductor technology. His lab develops first-principles electronic structure methods integrated with molecular dynamics, Monte Carlo, and machine learning algorithms (including deep learning and Gaussian processes). The primary goal is to bridge the microscopic quantum world with macroscopic material properties. Applications range from designing efficient CO₂ conversion catalysts to improving fuel cell performance. His recent focus on AI-enhanced materials design supports the global drive toward a carbon-neutral society. By decoding physical origins of material behavior, he provides theoretical guidelines for improving functionality, efficiency, and sustainability. His comprehensive approach offers insights into both fundamental and applied materials science.

📚Publication Top Notes

1. Experimental and Theoretical Investigations on pH-Dependent Molecular Structure, Electronic Structure, and Absorption Spectra of Ruthenium(II) Complexes with Extended Ligand

Journal of Molecular Structure, November 2025
Contributors: Zi Ying Yeoh, Yoshitada Morikawa, Siow-Ping Tan, Mohammad B. Kassim, Siew San Tan
Summary: This work combines experimental spectroscopy and first-principles simulations to analyze how pH variation influences the molecular geometry and electronic structure of ruthenium(II) complexes. The study demonstrates that protonation states significantly affect the absorption spectra, providing insights into their electronic transitions and potential in sensing and catalytic applications.

2. VibIR-Parallel-Compute: Enhancing Vibration and Infrared Analysis in High-Performance Computing Environments

Journal of Open Source Software, April 15, 2025
Contributors: Kurt Irvin M. Rojas, Yoshitada Morikawa, Ikutaro Hamada
Summary: This publication presents a new open-source computational tool designed to improve the efficiency of vibrational and infrared spectral analysis in large-scale simulations. The tool utilizes parallel computing to accelerate data processing, enabling high-throughput simulations of complex molecular systems in quantum chemistry and materials research.

3. Stabilization of Oxygen Vacancy Ordering and Electrochemical-Proton-Insertion-and-Extraction-Induced Large Resistance Modulation in Strontium Iron Cobalt Oxides Sr(Fe,Co)Oₓ

Nature Communications, January 2, 2025
Contributors: Yosuke Isoda, Thanh Ngoc Pham, Ryotaro Aso, Shuri Nakamizo, Takuya Majima, Saburo Hosokawa, Kiyofumi Nitta, Yoshitada Morikawa, Yuichi Shimakawa, Daisuke Kan
Summary: This collaborative study investigates resistance changes in Sr(Fe,Co)Oₓ caused by reversible proton insertion and oxygen vacancy ordering. Using both experimental data and theoretical modeling, it uncovers mechanisms relevant to next-generation memory and switching devices based on complex oxides.

4. CO Hydrogenation Promoted by Oxygen Atoms Adsorbed onto Cu(100)

Journal of Physical Chemistry C, 2024
Contributors: K. Nagita, K. Kamiya, S. Nakanishi, Y. Hamamoto, Y. Morikawa
Summary: This research explores how the presence of adsorbed oxygen atoms on a copper (100) surface alters the catalytic pathway for carbon monoxide hydrogenation. The study combines surface science experiments and density functional theory to propose a more efficient CO-to-methanol conversion mechanism, relevant for sustainable fuel production.

5. Effect of Fluorine Substitution on the Electronic States and Conductance of CuPc on Cu(100)

Applied Surface Science, 2024
Contributors: H. Okuyama, S. Kuwayama, S. Hatta, T. Aruga, Y. Hamamoto, T. Shimada, I. Hamada, Y. Morikawa
Summary: This paper investigates the electronic behavior of copper phthalocyanine (CuPc) molecules modified with fluorine atoms when adsorbed on a Cu(100) surface. The study reveals how fluorine substitution modifies the molecule–metal interaction, enhancing electronic tunability for organic semiconductor and device engineering applications.

🏆 Conclusion 

Professor Yoshitada Morikawa is highly suitable for the Best Researcher Award, especially for awards that prioritize:

  • Long-term scholarly excellence,

  • Interdisciplinary research, and

  • Cutting-edge integration of AI with quantum materials science.

His career is marked by rigorous academic scholarship, leadership in the scientific community, and a forward-looking research agenda tackling environmental and energy-related grand challenges.