Xichao Gao | Hydrology | Best Researcher Award

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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.

 

 

Bo Zhang | Environmental Science | Best Research Article Award

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Mr. Bo Zhang | Environmental Science | Best Research Article Award

Mr. Bo Zhang | Environmental Science | Associate Professor at Northwestern Polytechnical University | China

Mr. Bo Zhang is an accomplished researcher whose career reflects a distinguished trajectory in the fields of remote sensing, geoinformation science, and artificial intelligence applications for environmental monitoring, and he has consistently demonstrated excellence through impactful research contributions and global collaborations. Educated with a doctoral degree in Remote Sensing from a leading academic institution, Mr. Bo Zhang has established a solid academic foundation that underpins his expertise in applying advanced computational methods to real-world environmental challenges. His professional experience includes multiple collaborative projects with internationally recognized scholars from institutions such as Tsinghua University, Southeast University, Technical University of Munich, University of Hong Kong, and others, through which he has advanced the use of deep learning techniques, satellite image downscaling, and GIS-based data integration for earth observation and climate-related studies. Mr. Bo Zhang’s research interests lie primarily in satellite remote sensing, super-resolution reconstruction of geospatial datasets, atmospheric and environmental data analysis, epidemiological mapping, and the integration of machine learning for improved predictive accuracy in public health and ecological monitoring, reflecting an interdisciplinary approach that combines computing, earth science, and applied technology. His research skills span deep learning model development, neural network applications for image processing, spatial epidemiology analysis, super-resolution algorithms, and the integration of volunteered geographic information into traditional mapping platforms, enabling him to contribute both theoretically and practically to geospatial sciences.

Academic Profile: ORCID | Scopus | Google Scholar

Featured Publications:

  • Zhu, B., Liu, J., Fu, Y., Zhang, B., & Mao, Y. (2018). Spatio-temporal epidemiology of viral hepatitis in China (2003–2015): Implications for prevention and control policies. International Journal of Environmental Research and Public Health, 15(4), 661. Citations: 73.

  • Pan, D., Zhang, M., & Zhang, B. (2021). A generic FCN-based approach for the road-network extraction from VHR remote sensing images—Using OpenStreetMap as benchmarks. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Citations: 59.

  • Zhang, B., Zhang, M., Kang, J., Hong, D., Xu, J., & Zhu, X. (2019). Estimation of PMx concentrations from Landsat 8 OLI images based on a multilayer perceptron neural network. Remote Sensing, 11(6), 646. Citations: 29.

  • Zhang, B., Xiong, W., Ma, M., Wang, M., Wang, D., Huang, X., Yu, L., Zhang, Q., … (2022). Super-resolution reconstruction of a 3 arc-second global DEM dataset. Science Bulletin, 67(24), 2526–2530. Citations: 25.