Alima Amangeldi | Climate Change | Research Excellence Award

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Ms. Alima Amangeldi | Climate Change | Research Excellence Award

Ms. Alima Amangeldi | Climate Change | Engineer at Institute of Ionosphere | Kazakhstan

Climate Change research forms the foundation of the scholarly and professional journey of Ms. Alima Amangeldi, an emerging Kazakhstani researcher whose contributions reflect a strong commitment to environmental resilience, cryosphere monitoring, and long-term hydro-climatic analysis. Ms. Alima Amangeldi is recognized for her intellectual dedication to understanding glacial dynamics and climate-driven transformations across mountainous regions, with a special focus on the Ile-Alatau Mountains within the Northern Tien Shan. Her academic background is rooted in rigorous scientific training in environmental science, Earth observation, and atmospheric studies, equipping her with a deep understanding of hydrology, remote sensing technologies, and climate processes that shape contemporary environmental challenges. Through her education, Ms. Alima Amangeldi developed expertise in satellite imagery interpretation, GIS-based glacier mapping, and the multi-temporal analysis of glacial and moraine lake changes that are vital for predicting natural hazards and informing adaptation strategies. Professionally, she serves at the Institute of Ionosphere in Almaty, Kazakhstan (2024–present), where she contributes to advanced research on atmospheric interactions, hydro-climatic variability, and geospatial environmental assessments. Her role involves conducting multi-temporal remote sensing studies, applying high-resolution climatic datasets, and collaborating with multidisciplinary experts to deepen insights into mountain cryosphere evolution and its broader regional implications. The professional experience of Ms. Alima Amangeldi is marked by her capacity to integrate field observations, satellite-based analytics, and climate modeling, which positions her as a promising researcher in climate science and natural resource management. Her research interests span climate change impacts on glacier recession, moraine lake expansion, hydro-climatic risk assessments, cryosphere monitoring, atmospheric variation analysis, and remote sensing applications in mountainous environments. In her work, Ms. Alima Amangeldi frequently employs advanced research skills such as geospatial analytics, LiDAR interpretation, multi-decadal climate data synthesis, hydrological modeling, and environmental trend analysis. She is proficient in utilizing tools like ArcGIS, QGIS, Google Earth Engine, ERDAS Imagine, and statistical packages used for climatic trend evaluation. Her analytical skills are further supported by her ability to interpret long-term climate indicators and correlate them with environmental shifts across sensitive alpine ecosystems. Although early in her academic trajectory, Ms. Alima Amangeldi has begun building an impactful scholarly presence, evidenced by her authorship of peer-reviewed scientific work focusing on long-term glacier and lake evolution from 1955–2024, informing early-warning systems, hazard prevention measures, and sustainable water resource planning. Her dedication to scientific rigor and environmental protection has earned her recognition as a promising contributor to Kazakhstan’s climate research community, and she is increasingly acknowledged within international research circles addressing cryosphere change and climate-induced mountain hazards. Awards and honors attributed to her include early-career recognition at institutional and departmental levels for excellence in scientific research and contributions to climate-related geospatial studies. Throughout her career progression, Ms. Alima Amangeldi continues to commit herself to advancing the scientific understanding of climate change impacts, strengthening the data foundations needed for sustainable policymaking, and contributing meaningful insights to global environmental research dialogues. In conclusion, Ms. Alima Amangeldi exemplifies the next generation of climate and environmental scientists whose research promises to support long-term climatic resilience, inform public awareness, and guide actionable strategies for adapting mountain ecosystems to the realities of a rapidly changing climate.

Academic Profile: ORCID

Featured Publications:

  1. Amangeldi, A. A., Iskaliyeva, G., Merekeyev, A., Sydyk, N., Abishev, B., & Baygurin, Z. (2025). Hydro-Climatic and Multi-Temporal Remote Analysis of Glacier and Moraine Lake Changes in the Ile-Alatau Mountains (1955–2024), Northern Tien Shan. Atmosphere.

 

 

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

 

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.