Daniel Eshimiakhe | Environmental Science | Editorial Board Member

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Dr. Daniel Eshimiakhe | Environmental Science | Editorial Board Member

Researcher | Ahmadu Bello University | Nigeria

Dr. Daniel Eshimiakhe is a Nigerian geophysicist and researcher at Ahmadu Bello University, Zaria, specializing in electrical resistivity, seismology, magnetics, and machine-learning–enhanced geophysical analysis. His work focuses on subsurface characterization, mineral exploration, groundwater assessment, and environmental geophysics, with applications that support public health, sustainable resource management, and geological risk evaluation. He has authored and co-authored 28 scientific publications, accumulating over 109 citations, an h-index of 7, and an i10-index of 3, reflecting a growing influence in applied geosciences.Eshimiakhe’s research portfolio spans a wide range of geophysical techniques, including 2D/3D electrical resistivity tomography, seismic refraction tomography, aeromagnetic data interpretation, and geoelectrical modeling. His notable works include studies on landfill leachate migration, seasonal geochemistry impacts on groundwater systems, mineshaft imaging, kaolin deposit delineation, and gold mineral potential zone mapping across Northern Nigeria. His papers in Scientific Reports contributed significantly to groundwater potential mapping and environmental monitoring using integrated geoelectrical and geochemical approaches.He has collaborated extensively with multidisciplinary teams across Nigeria, contributing to research that integrates remote sensing, open-source geophysical modeling, and unsupervised machine learning, particularly in the refinement of Werner deconvolution for geologic depth estimation. His early work also includes participation in international conferences such as the AGU Fall Meeting, where he presented findings on subsurface cavity detection and mineral feature mapping through aeromagnetic techniques.Beyond academic contributions, Eshimiakhe’s research has substantial societal relevance, informing mining safety, infrastructure development, groundwater management, and environmental protection in resource-sensitive regions. His commitment to advancing geophysical science through innovative methodologies positions him as an emerging figure in applied Earth sciences, with ongoing work contributing to data-driven geological interpretation and sustainable natural resource exploration.

Profiles : Googlescholar | Scopus 

Featured Publications

1.Katz, D. H., Tahir, U. A., Ngo, D., Benson, M., Bick, A. G., Pampana, A., Gao, Y., … & Eshimiakhe, D. (2020). Proteomic profiling in biracial cohorts implicates DC-SIGN as a mediator of genetic risk in COVID-19. MedRxiv. Cited By: 23

2.Alao, J. O., Otorkpa, O. J., Abubakar, F., Eshimiakhe, D., Aliyu, A., Abdulsalami, M., … & [Other authors if applicable]. (2024). Geoelectrical resistivity and geochemistry monitoring of landfill leachates due to the seasonal variations and the implications on groundwater systems and public health. Scientific Reports, 14(1), 26542. Cited By: 20

3.Osumejeh, J. O., Eshimiakhe, D., Kudanmya, E. A., Ojo, F., & Lawal, K. M. (2023). Geophysical investigation of part of Ahmadu Bello University farm, Nigeria. African Scientific Reports, 49, 49-49. Cited By: 13

4.Osumeje, J. O., Eshimiakhe, D., Oniku, A. S., & Lawal, K. M. (2024). Application of remote sensing and electrical resistivity technique for delineating groundwater potential in North Western Nigeria. Scientific Reports, 14(1), 22299.  Cited By: 8

5.Osumeje, J., Eshimiakhe, D., Bello, Y., & Lawal, K. (2023). Assessment of open-software resources in Python using real two-dimensional geophysical model. SSRN. Cited By: 8

Daniel Eshimiakhe’s research integrates geophysical techniques, remote sensing, and machine learning to advance subsurface characterization, mineral exploration, and groundwater assessment. His work informs sustainable resource management, environmental protection, and public health, bridging scientific innovation with societal and industrial applications.

Tarun Kumar Lohani | Environmental and Sustainable Materials | Best Academic Researcher Award

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Prof. Tarun Kumar Lohani | Environmental and Sustainable Materials | Best Academic Researcher Award

Professor | Arba Minch University | India

Prof.  Tarun Kumar Lohani is a distinguished Professor at the Arba Minch Water Technology Institute (AWTI), Arba Minch University, Ethiopia, specializing in Water Technology, hydrological modelling, groundwater systems, climate-change–induced water challenges, GIS–based environmental analysis, and machine-learning applications in hydrology. With an extensive research portfolio comprising 119 scholarly documents, 1063 citations, an h-index of 16, and 31 i10-index publications, he has established himself as a leading contributor to contemporary water resource science and sustainability research.Prof. Lohani’s early work on construction materials, particularly the highly cited study on the optimum utilization of quarry dust as a partial replacement of sand in concrete 143 citations demonstrated his ability to bridge engineering materials research with green and cost-efficient construction practices. Over the years, his scholarship has evolved toward advanced hydrological systems climate resilience and watershed modelling. His research contributions span critical themes such as flood susceptibility mapping using multi-criteria decision-making and machine learning 70 citations groundwater quality assessment in Ethiopia’s major basins sediment transport modelling using HEC-RAS and the application of the WetSpass and SWAT models for surface–subsurface water balance and sediment yield simulations.A hallmark of his career is his strong interdisciplinary and international collaboration reflected in joint publications with researchers across India Ethiopia China and multiple global institutions. His work intersects water resource engineering environmental science remote sensing and emerging computational techniques. Notable contributions include hydrodynamic modelling of climate-change scenarios reservoir operation optimization MIKE11-NAM streamflow simulations for data-scarce regions LULC change detection using SVM and ANN models and the integration of metaheuristic and machine-learning approaches in agricultural and environmental systems.Through his research Prof. Lohani has significantly advanced understanding of sustainable water management climate-induced hydrological risks and water security in vulnerable regions. His work supports government agencies local communities and international stakeholders in improving disaster preparedness groundwater sustainability and environmental planning. Recognized for scientific rigor collaborative leadership and societal impact Prof. Lohani continues to contribute meaningfully to global water research and capacity building in the Horn of Africa.

Profiles : ORCID | Scopus | Google Scholar | ResearchGate

Featured Publications

1.Lohani, T. K., Padhi, M., Dash, K. P., & Jena, S. (2012). Optimum utilization of quarry dust as partial replacement of sand in concrete. International Journal of Applied Science and Engineering Research, 1(2), 391–404. Cited by : 143

2.Rakhra, M., Singh, R., Lohani, T. K., & Shabaz, M. (2021). Metaheuristic and machine learning-based smart engine for renting and sharing of agriculture equipment. Cited by : 82

3.Sharma, C., Amandeep, B., Sobti, R., Lohani, T. K., & Shabaz, M. (2021). A secured frame selection based video watermarking technique to address quality loss of data: Combining graph-based transform, singular value decomposition, and hyperchaotic. Cited By : 57

4.Yao, Q., Shabaz, M., Lohani, T. K., Bhatt, M. W., Panesar, G. S., & Singh, R. K. (2021). 3D modelling and visualization for vision-based vibration signal processing and measurement. Cited By : 57

5.Ukumo, T. Y., Abebe, A., Lohani, T. K., & Edamo, M. L. (2023). Flood hazard mapping and analysis under climate change using hydro-dynamic model and RCPs emission scenario in Woybo River catchment of Ethiopia.
Cited By : 32

Prof. Lohani’s work advances hydrological modelling and climate-resilient water management, providing scientifically robust tools that strengthen water security, disaster preparedness, and sustainable resource planning for vulnerable regions.