Back to Search
Profile photo of Abraham Aidoo

Abraham Aidoo

Mathematics

View Official KNUST Profile

About

Staff has not updated content!

Research Summary

(inferred from publications by AI)

The researcher has made significant contributions to advancing remote sensing and LiDAR applications in various domains, including forest biomass estimation, CEOR techniques, reservoir engineering, hydrocarbon exploration, and urban heat island mitigation. Their work emphasizes the development of integrated methodologies that bridge geospatial data analysis with subsurface characterization and numerical modeling, providing a comprehensive approach to addressing challenges across physical sciences.

Research Themes

All Papers

LIDAR-Based Forest Biomass Remote Sensing: A Review of Metrics, Methods, and Assessment Criteria for the Selection of Allometric Equations(2023)
Machine learning models for subtropical forest aboveground biomass mapping using combined SAR and optical satellite imagery(2025)
Development of hybrid low salinity water polymer flooding numerical reservoir simulator and smart proxy model for chemical enhanced oil recovery (CEOR)(2019)
Critical evaluation of infill well placement and optimization of well spacing using the particle swarm algorithm(2019)
Integrated Reservoir Characterisation for Petrophysical Flow Units Evaluation and Performance Prediction(2019)
Analysis of Spatial Distribution Pattern of Reservoir Petrophysical Properties for Horizontal Well Performance Evaluation-A Case Study of Reservoir X(2019)
Mapping of Porosity, Permeability and Thickness Distribution: Application of Geostatistical Modeling for the Jubilee Oilfield in Ghana(2019)
Spatio-temporal land use/land cover change analysis and assessment of urban heat island in Ghana: A focus on the Greater Accra Region(2024)

Collaboration Network

No collaborations found in the dataset.

About This Profile

This profile is generated from publicly available publication metadata and is intended for research discovery purposes. Themes, summaries, and trajectories are inferred computationally and may not capture the full scope of the lecturer's work. For authoritative information, please refer to the official KNUST profile.