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.

Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

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Mr. Nuttapat Jittratorn | Renewable Energy | Best Researcher Award

Ph.D. Candidate in Electrical Engineering, National Cheng Kung University, Taiwan.

Nuttapat Jittratorn is a passionate Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan. With a deep-rooted commitment to renewable energy innovation, he has led over 10 collaborative projects across Taiwan and Japan, applying AI to enhance energy forecasting systems. His academic and industrial experience spans solar PV, wind power, and hybrid energy systems. Nuttapat’s interdisciplinary expertise merges machine learning with real-time deployment, helping industries such as TSMC and Delta Electronics optimize energy use. Recognized with the Best Oral Presentation Award at the 2025 IEEE IAS Annual Meeting, he also contributes to academic leadership as a session chair and student mentor. A forward-thinking researcher fluent in English and Thai, he continues to bridge research with sustainable industrial solutions.

🧾Author Profile

🎓 Education

Nuttapat Jittratorn began his academic journey at Kasetsart University, Thailand, earning a Bachelor of Engineering in Electrical Engineering (2014–2018). He then pursued his Master’s degree at National Chung Cheng University in Taiwan, where he deepened his focus on renewable energy systems and intelligent computation (2018–2021). Currently, he is a Ph.D. candidate in Electrical Engineering at National Cheng Kung University, Taiwan (2021–present). His doctoral research centers on enhancing the reliability and accuracy of energy forecasting using artificial intelligence. Throughout his studies, Nuttapat has maintained a strong interdisciplinary approach, integrating engineering principles with emerging technologies like deep learning and hybrid modeling. His academic path reflects a consistent commitment to solving global energy challenges through intelligent system design and applied machine learning in energy grids.

💼 Experience 

Since 2021, Nuttapat has played pivotal roles as Team Leader, Project Advisor, and Researcher across Taiwan and Japan. He has collaborated with leading institutions and corporations such as TSMC, Delta Electronics, FarEasTone Telecom, and the National Science and Technology Council. His work involves real-time AI-powered forecasting systems for solar, wind, and multi-load applications in power and steam. Nuttapat has led the development and deployment of models in real-world industrial settings, optimizing power generation and usage. As a Thesis Advisor at Ton Duc Thang University (2022–2023), he mentored students in AI-energy research and thesis defense preparation. His projects span Changhua, Hsinchu, Tainan, Taoyuan, and Kagoshima, showcasing his ability to drive innovation in dynamic, multinational environments.

🏅 Honors & Awards 

Nuttapat Jittratorn was awarded the Best Oral Presentation Award in the Renewable and Sustainable Energy Conversion track at the 2025 IEEE IAS Annual Meeting, recognizing his research impact in intelligent PV and wind power forecasting. Additionally, he served as the Session Chair at the same Award, a testament to his leadership and recognition in the energy research community. His collaborative research and advisory roles in academia and industry have positioned him as a standout researcher in applied energy systems. These achievements underscore his ability to produce not just high-quality publications, but also real-world, industry-transforming outcomes that align with global sustainability goals.

🔬 Research Focus 

Nuttapat’s research is centered on AI-based renewable energy forecasting. He develops intelligent models for very short-term and short-term prediction of solar PV and wind power generation. His focus includes hybrid techniques that combine LSTM, Markov models, and probabilistic correction based on environmental data like wind speed. He also explores energy storage integration, such as BESS (Battery Energy Storage Systems), to enhance operational efficiency. His work bridges data science and engineering, ensuring models are not only accurate in labs but also viable for real-world deployment in industrial energy management. His interdisciplinary projects support Taiwan and Japan’s energy industries in transitioning toward smarter and more reliable grid systems. His research is forward-looking, contributing directly to the goals of a low-carbon economy and sustainable industrial operations.

Publication Top Notes

1. A Hybrid Method for Hour-Ahead PV Output Forecast with Historical Data Clustering

Authors: N. Jittratorn, G.W. Chang, G.Y. Li
Conference: 2022 IET International Conference on Engineering Technologies
Citations: 4
Summary: This paper proposes a clustering-based hybrid model for predicting hour-ahead PV output. Historical meteorological data are clustered to create more accurate baseline patterns, improving forecast accuracy. The model has industrial applications for solar plant operation scheduling.

2. Very Short-Term Wind Power Forecasting Using a Hybrid LSTM-Markov Model Based on Corrected Wind Speed

Authors: A.N. Jittratorn, B.C.M. Huang, C.H.T. Yang
Journal: Renewable Energy and Power Quality Journal, Vol. 21, pp. 433–438
Year: 2023 | Citations: 2
Summary: A hybrid forecasting framework combining LSTM and a Markov decision structure, this study corrects input wind speed for improving wind power forecasts within minutes to hours. Effective for wind turbine operational control and energy market participation.

3. A Deterministic and Probabilistic Framework Based on Corrected Wind Speed to Improve Short-Term Wind Power Forecasting Accuracy

Authors: N. Jittratorn, C.M. Huang, H.T. Yang
Journal: International Journal of Electrical Power & Energy Systems, Vol. 170, 110859
Year: 2025
Summary: This journal article presents an advanced dual-framework model integrating deterministic forecasts with probabilistic corrections, improving reliability in fluctuating wind environments. It’s particularly useful for risk-aware grid management and dispatch.

4. Short-Term Forecasting of Wind Power Plant Generation Based on Machine Learning Models

Authors: M.N. Phan, K.P. Nguyen, V. Van Huynh, C.M. Huang, H.T. Yang, N. Jittratorn, et al.
Conference: 2025 IEEE 1st International Conference on Smart and Sustainable Developments
Year: 2025
Summary: Collaborative paper exploring various machine learning models for short-term wind forecasting. Nuttapat contributed to model selection, tuning, and integration with real-time plant data.

5. PV Power Forecasting for Operation of BESS Integrated with a PV Generation Plant

Authors: N. Jittratorn, C.S. Liu, C.M. Huang, H.T. Yang
Conference: 2024 IEEE 19th Conference on Industrial Electronics and Applications (ICIEA)
Year: 2024
Summary: Proposes a new forecasting model to manage PV+BESS operation, ensuring optimal battery use while minimizing forecast error. Critical for smart energy storage deployment in renewable infrastructure.

🏅 Conclusion

Nuttapat Jittratorn is a highly promising early-career researcher with solid technical, academic, and leadership credentials. His contributions to AI-driven energy forecasting and integration with industrial applications stand out. While still in the Ph.D. phase, his research maturity, real-world impact, and academic service position him as a strong candidate for the Best Researcher Award, particularly in the applied energy systems or smart grid technologies domain.