About

I am a Doctor of Philosophy candidate in Civil Engineering at the University of Kentucky, where I serve as a Graduate Teaching and Research Assistant. I hold a Master of Science degree in Civil Engineering from Tennessee Technological University and a Bachelor of Science degree in Civil Engineering from Kwame Nkrumah University of Science and Technology, Kumasi, where I graduated with honors and received multiple awards for excellence. My core competencies include machine learning, predictive modeling, deep learning, water distribution modeling, and hydrological modeling.

During the summer of 2024, I worked as a Technical/Paraprofessional at the University of Kentucky. In this role, I developed and optimized water distribution models for utilities in Eastern Kentucky, collected and analyzed field data, including water quality parameters and pressure readings, and applied machine learning techniques to large datasets to extract functional relationships for water distribution modeling. I also assisted in organizing and conducting regional and utility-focused training workshops on water distribution systems.

Previously, as a Graduate Research Assistant at Tennessee Technological University, I developed a suite of machine learning and deep learning models tailored for flood forecasting and water resource management. Key projects included creating a deep learning-based flood forecasting tool using the Firefly Algorithm for Cummins Falls State Park and an LSTM-based flood prediction system with transfer learning techniques for watersheds in Tennessee and Kentucky. I also developed flood early warning systems for the Elk River watershed and Ashland City, Tennessee, leveraging genetic algorithms and high-performance computing (HPC) clusters equipped with GPUs.

I am passionate about leveraging cutting-edge machine learning and deep learning techniques to revolutionize water resources engineering, enhance public health and safety, and contribute to sustainable infrastructure development. I am eager to collaborate with interdisciplinary teams to drive impactful, data-driven solutions for pressing water resource issues.

Skills

• Programming Languages: Python, R, Matlab

• Frameworks: Scikit, TensorFlow, Keras, Pytorch

• Softwares: HEC-HMS, ArcGIS, HEC-RAS, KYPIPE, EPANET

• Soft Skills: Leadership, Event Management, Writing, Public Speaking

Research Projects

• Developed a deep learning-based flood early warning system with genetic algorithm for the Elk River watershed.

• Developed LSTM based flood prediction models with transfer learning techniques for watersheds in Tennessee and Kentucky.

• Developed a flood water level forecasting system using LSTM, 1D and 2D CNN deep learning architectures for watersheds in Tennessee and Kentucky.

• Developed a deep learning-based flood forecasting tool with the Firefly Algorithm for the Cummins Falls State Park.

• Developed a flood early warning system for Ashland City, Tennessee using Machine Learning techniques such as Random Forest, Support Vector Machines.

• Developed 3D CNN models to estimate porosity values from 3D images.

• Developed a configuration graphical user interface (GUI) in Python for a Machine Learning-based flood forecaster model for the Window Cliffs State Park, Tennessee.

• Developed a data preprocessing module in Python for a Machne Learning-based flood forecaster model.

• Developed a data conversion tool in Python for converting large time series data to images for 2D-CNN deep learning models.

• Utilized Graphic Processing Units (GPUs) in High Performance Computing (HPC) clusters for Machine Learning model development.

• Developed a HEC-HMS hydrological model for the Blackburn Fork Watershed in Tennessee.

Awards and Honors

• First Place Poster Award Winner- 33rd American Water Resources Conference (Tennessee Section)- 2024

• Outstanding Graduate Leader Award Winner- TTU Student Leaders Awards-2024

• People’s Choice Poster Award Winner- 32nd American Water Resources Conference (Tennessee Section)- 2023

• Ghana Young Achievers Award- Outstanding Young Achiever in Engineering 2021

• Ghana Students Awards – Best Student MC Nominee 2021

• KNUST College of Engineering – Provost Excellent Student Award – 2020/2021

• KNUST College of Engineering – Provost Excellent Student Award – 2019/2020

• KNUST College of Engineering-Provost Excellent Student Award- 2017/2018

Publications and Conferences

• Fordjour, G.K., Nur, F., Kalyanapu, A.J. et al. Enhancing flood water level prediction through transfer learning with warmup learning rate scheduling in LSTM models: a comparative case study in Kentucky and Tennessee watersheds. Model. Earth Syst. Environ. 11, 14 (2025). https://doi.org/10.1007/s40808-024-02211-z.

• Fordjour, George Kwabena. “Development of a Deep Learning-Based Flood Forecasting Tool with Firefly Algorithm for the Blackburn Fork Watershed, Tennessee.” Tennessee Technological University ProQuest Dissertations & Theses (2024).

• Fordjour, George K., and Alfred J. Kalyanapu. “Implementing a GRU Neural Network for Flood Prediction in Ashland City, Tennessee.” arXiv preprint arXiv:2405.10375 (2024).

• Fordjour, G. K., and Kalyanapu, A. J. (2024). “Transfer Learning Approach for Flood Forecasting using Deep Learning based Models”. 19th Annual Research and Creative Inquiry Day, Tennessee Technological University, Cookeville, TN.

• Fordjour, G. K., Darkwah, G. K., and Kalyanapu, A. J. (2024). “Flood early warning systems using machine learning techniques: The case of the Cumberland River at Ashland City, Tennessee”. 19th Annual Research and Creative Inquiry Day, Tennessee Technological University, Cookeville, TN.

• Fordjour, G. K., and Kalyanapu, A. J. (2024). “Deep Learning Based Flood Early Warning System with Firefly Algorithm for The Blackburn Fork Watershed, Tennessee”, 33rd Tennessee Water Resources Symposium, Proc., 2024 Tennessee Water Resources Symposium, TNAWRA, Montgomery Bell State Park, April 2-4, 2024, Burns, TN. (won first place award).

• Fordjour, G. K., Darkwah, G. K., and Kalyanapu, A. J. (2023). “Flood early warning systems using machine learning techniques: The case of the Cumberland River at Ashland City, Tennessee”, 32nd Tennessee Water Resources Symposium, Proc., 2023 Tennessee Water Resources Symposium, TNAWRA, Montgomery Bell State Park, April 12-14, 2023, Burns, TN. (won people’s choice award).