Ali Haji Elyasi | Earth Sciences | Best Researcher Award

Mr. Ali Haji Elyasi | Earth Sciences | Best Researcher Award

Mr. Ali Haji Elyasi | Earth Sciences | PhD at University of Tehran | Iran

Mr. Ali Haji Elyasi is a researcher in Civil and Water Resources Engineering with a strong academic foundation and applied research orientation in hydrological systems, groundwater sustainability, and hydraulics. He is pursuing his Ph.D. in Civil Engineering (Water and Hydraulic Structures) at the University of Tehran, where he has developed expertise in advanced hydro-environmental modeling, geospatial intelligence, machine learning applications, and remote sensing-driven environmental monitoring. His education combines rigorous theoretical training with hands-on field research, enabling him to address complex challenges related to groundwater quality, flood risk assessment, watershed hydrology, and wetland ecosystem dynamics. Professionally, Mr. Ali Haji Elyasi has contributed to several interdisciplinary research projects in collaboration with leading institutions and research groups, focusing on groundwater potential analysis, land-use change detection, aquifer vulnerability assessment, and integrated water resource management strategies. His research interests include sustainable hydrology, resilience-based water infrastructure planning, satellite-based environmental observation, climate impact assessments, and data-driven decision support systems in semi-arid and ecologically sensitive regions. He is skilled in GIS, Remote Sensing, machine learning modeling, hydrological simulation, spatial data analysis, and environmental data interpretation, and he consistently integrates computational methods to enhance predictive accuracy and resource planning outcomes. Mr. Ali Haji Elyasi has published multiple peer-reviewed articles indexed in Scopus and reputable engineering journals, and his scientific contributions have been recognized through increasing citations and scholarly collaborations across universities and research institutions. He has also engaged in academic community development through teamwork, joint authorships, mentoring, and research dissemination activities. Awards and recognitions relate to his contributions in water resource research, collaborative scientific output, and commitment to advancing environmental sustainability. Overall, Mr. Ali Haji Elyasi demonstrates a strong commitment to scientific integrity, research innovation, and practical solutions for sustainable water system management, positioning him as a promising researcher capable of contributing meaningfully to academic scholarship, environmental policy, infrastructure planning, and future scientific leadership.

Academic Profile: ORCID | Google Scholar

Featured Publications:

Eftekhari, M., Mobin, E., Akbari, M., & Elyasi, A. H. (2021). Application assessment of GRACE and CHIRPS data in the Google Earth Engine to investigate their relation with groundwater resource changes (Northwestern region of Iran). Journal of Groundwater Science and Engineering, 9(2), 102–113. Cited 18 times

Eftekhari, M., Eslaminezhad, S. A., Akbari, M., DadrasAjirlou, Y., & Elyasi, A. H. (2021). Assessment of the potential of groundwater quality indicators by geostatistical methods in semi-arid regions. Journal of Chinese Soil and Water Conservation, 52(3), 158–167. Cited 7 times

Eslaminezhad, S. A., Eftekhari, M., Mahmoodizadeh, S., Akbari, M., & Elyasi, A. H. (2021). Evaluation of tree-based artificial intelligence models to predict flood risk using GIS. Iran-Water Resources Research, 17(2), 174–189. Cited 7 times

Eftekhari, M., Eslaminezhad, S. A., Elyasi, A. H., & Akbari, M. (2021). Geostatistical evaluation with Drinking Groundwater Quality Index (DGWQI) in Birjand Plain aquifer. Environment and Water Engineering, 7(2), 267–278. Cited 7 times

Eslaminezhad, S. A., Eftekhari, M., Akbari, M., Elyasi, A. H., & Farhadian, H. (2022). Predicting flood-prone areas using advanced machine learning models (Birjand Plain). Water and Irrigation Management, 11(4), 885–904. Cited 3 times

 

Xichao Gao | Hydrology | Best Researcher Award

Dr. Xichao Gao | Hydrology | Best Researcher Award

Dr. Xichao Gao | Hydrology | Senior Engineer at China Institute of Water Resources and Hydropower Research | China

Gr. Xichao Gao is an eminent researcher specializing in hydrology, water resources, and climate-driven environmental modeling, with a strong focus on urban waterlogging, drought recovery, and real-time rainfall estimation using advanced computational and deep learning methods. He obtained his Ph.D. in Hydrology and Water Resources from a leading Chinese research university, which provided a solid foundation for his extensive contributions to the China Institute of Water Resources and Hydropower Research in Beijing, China, where he currently serves as a leading researcher. Over his career, Gr. Gao has published 30 high-impact documents, which have collectively received 403 citations from 356 documents, reflecting his scholarly influence and an h-index of 12. His professional experience encompasses leading and participating in multinational research projects addressing climate resilience, hydrological risk management, and sustainable urban water systems. Gr. Gao’s research interests include hydrological process modeling, environmental monitoring using video-based and remote-sensing techniques, AI-driven water resource management, and climate adaptation strategies. He possesses advanced research skills in machine learning applications in hydrology, statistical modeling, image-based waterlogging analysis, and integrated assessment of drought recovery processes, which have enabled him to produce innovative methodologies recognized internationally. His work has appeared in reputed journals such as Hydrological Processes, Environmental Modelling and Software, and Measurement: Journal of the International Measurement Confederation, highlighting his commitment to advancing high-quality scientific knowledge. In addition to his research output, Gr. Gao has demonstrated leadership through mentoring early-career researchers, contributing to community-based water resilience initiatives, and serving on professional committees within hydrology and environmental engineering societies. He has received multiple research honors and recognitions for his contributions to climate-adaptive hydrology and computational water modeling, reflecting his growing reputation as a leading scholar in his field.

Academic Profile: Scopus

Featured Publications:

  1. Gao, X. (2026). Measuring urban waterlogging depth from video images using human body models. Measurement: Journal of the International Measurement Confederation. 0 citations.

  2. Gao, X., et al. (2025). A framework to quantify drought recovery time accounting for the lagged effect. Hydrological Processes. 1 citation.

  3. Gao, X., et al. (2025). Real-time rainfall estimation using deep learning: Influence of background and rainfall intensity. Environmental Modelling and Software. 1 citation.