Ali Haji Elyasi | Earth Sciences | Best Researcher Award

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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

 

Hongtao Shi | Hydrology | Best Researcher Award

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Dr. Hongtao Shi | Hydrology | Best Researcher Award

Dr. Hongtao Shi | Hydrology | Lecturer at China University of mining and technology | China

Dr. Hongtao Shi is a lecturer at the School of Environment and Spatial Informatics, China University of Mining and Technology (CUMT) in Xuzhou, China, where he focuses on high-resolution microwave remote sensing of soil moisture, polarimetric SAR scattering modelling, and agricultural-hydrological applications of remote sensing data. He holds a Ph.D. in Photogrammetry and Remote Sensing from Wuhan University, China, where his doctoral research concentrated on multisource SAR and passive microwave methods for soil moisture retrieval; he also undertook joint doctoral training abroad at the University of Alicante, Spain. Prior to his current position, he completed his earlier degrees with an M.Sc. in Surveying Science and Technology from China University of Petroleum (East China) and a B.Sc. in Geographic Information Systems from the same institution. His professional experience includes his appointment at CUMT from mid-2021 onwards in the Environmental & Surveying Institute, during which time he has led and participated in national-level and laboratory-level research grants addressing multi‐angle, multi‐polarization SAR retrieval of soil moisture, high-resolution microwave downscaling, and airborne/spaceborne sensor data integration. His research interests span soil moisture inversion, multisource remote sensing for agriculture and hydrology, SAR polarimetry, passive microwave monitoring, time‐series image analysis, and machine-learning‐enhanced Earth-surface parameter retrieval. He has developed research skills in polarimetric SAR decomposition, multiscale data fusion, processing of microwave and optical remote sensing datasets, Python/Matlab/IDL/C# programming, time‐series modelling of hydrological variables, and uncertainty quantification in soil moisture retrieval. His honours include his role as Guest Editor for a special issue on “Soil Moisture Observation Using Remote Sensing and Artificial Intelligence” in the journal Remote Sensing, his membership in IEEE and the Chinese Society for Agricultural Meteorology, and his reviewer service for more than ten international journals including RSE, TGRS, JSTARS and Journal of Hydrology.

Academic Profile: ORCID | Scopus

Featured Publications:

Shi, H., Zhao, L., Yang, J., Lopez-Sanchez, J. M., Jinqi, Z., Sun, W., Lei, S., & Li, P. (2021). Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques. Remote Sensing of Environment, 261, 112485. (Citation 42)

Lang, F., Zhang, M., Zhao, J., Zheng, N., & Shi, H. (2024). Semantic segmentation for multisource remote sensing images incorporating feature slice reconstruction and attention upsampling.

Lang, F., Zhu, J., Qian, J., Dou, Q., Shi, H., Liao, L., & Zhao, L. (2025). Soil organic carbon estimation and transfer framework in agricultural areas based on spatiotemporal constraint strategy combined with active and passive remote sensing.

Zhao, J., Wang, Z., Sun, W., Yang, J., Shi, H., & Li, P. (2025). DMCF-Net: Dilated multiscale context fusion network for SAR flood detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

Zhao, J., Zhang, M., Zhou, Z., Wang, Z., Wang, F., Shi, H., & Zheng, N. (2025). CFFormer: A cross-fusion transformer framework for the semantic segmentation of multisource remote sensing images. IEEE Transactions on Geoscience and Remote Sensing.