Center: Center for Urban and Coastal Climate Science Research

Research Interests
Machine learning & AI applications to climate and environmental health

Disaster risk management (extreme weather—floods, droughts, heat waves, cold snaps—and disease-
vector infestations)

Climate–health linkages

Socioeconomic dimensions of neighborhood environmental degradation in underserved communities

Synoptic climatology

Entomology

Climate variability and change

Electric power systems & grid management (day-ahead price/demand forecasting, load balancing,
market bidding strategies)

Education
Ph.D., Physical Geography (Climate Science) — Julius-Maximilians-Universität Würzburg, Germany, 2023

M.Sc., Hydroscience and Engineering — Technische Universität Dresden, Germany, 2019

B.Tech., Applied Mathematics — Federal University of Technology, Owerri, Nigeria, 2014

Postdoc: Department of Geography, Kent State University (2023-2025)

Editorial Roles
Theoretical and Applied Climatology: Associate Editor (2024-present)
Discover Atmosphere: Associate Editor (2024-present)

Selected Publications

Ibebuchi, C. C., Abu, I. O., & Onwah, S. S. (2025). Environmental factors contributing to southern house
mosquito presence in Clark County, Nevada, using machine learning. Environmental Research
Communications, 7(6), 061005.

Ibebuchi, C. C. (2025). Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous
Predictors. Forecasting, 7(2), 18.

Ibebuchi, C. C., Richman, M. B., Obarein, O. A., Rainey, S., & Silva, A. (2025). Application of an artificial
neural network to improve understanding of the observed conterminous US winter precipitation
response to ENSO. Journal of Geophysical Research: Atmospheres, 130(7), e2024JD041735.

Ibebuchi, C. C., Akinyemi, O., & Abu, I. O. (2025). Selecting observationally constrained Global Climate
Model ensembles using autoencoders and transfer learning. Journal of Geophysical Research: Machine
Learning and Computation, 2(1), e2024JH000528.

Ibebuchi, C. C., & Abu, I. O. (2025). Probabilistic Flood Susceptibility Mapping Using Explainable AI for
the Western United States. Environmental Research Communications.

Ibebuchi, C. C. (2025). Uncertainty in machine learning feature importance for climate science: a
comparative analysis of SHAP, PDP, and gain-based methods. Theoretical and Applied Climatology,
156(9), 1-14.

Abu, I. O., & Ibebuchi, C. C. (2025). Risk assessment of the 2022 Nigerian flood event using remote
sensing products and climate data. Remote Sensing, 17(11), 1814.

Wegener, C., & Ibebuchi, C. C. (2025). Application of xgboost in disentangling the fingerprints of global
warming and decadal climate modes on seasonal precipitation trends in ohio. International Journal of
Climatology, 45(8), e8829.

Ibebuchi, C. C., & Richman, M. B. (2024). Deep learning with autoencoders and LSTM for ENSO
forecasting. Climate Dynamics, 62(6), 5683-5697.

Lee, C. C., Silva, A., Ibebuchi, C., & Sheridan, S. C. (2024). The influence of air masses on human mortality
in the contiguous United States. International journal of biometeorology, 68(11), 2281-2296.

Ibebuchi, C. C. (2024). Redefining the North Atlantic Oscillation index generation using autoencoder
neural network. Machine Learning: Science and Technology, 5(1), 01LT01.

Ibebuchi, C. C., & Lee, C. C. (2023). Global trends in atmospheric layer thickness since 1940 and
relationships with tropical and extratropical climate forcing. Environmental Research Letters, 18(10),
104007.

Ibebuchi, C. C., & Richman, M. B. (2023). Circulation typing with fuzzy rotated T-mode principal
component analysis: Methodological considerations. Theoretical and Applied Climatology, 153(1), 495-
523.