Machine Learning Prediction of Groundwater Contamination Vulnerability Using Hydrogeophysical Indicators in Ibadan

Oluwakemi Omolara Olukayode, Sakinat Folorunso, Olateju Bayewu, Odunayo Ojo, David Olukayode, Olubunmi Omotola, Adewale Sokan-Adeaga, Olayiwola Olaseeni
Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, PMLR 319:155-166, 2026.

Abstract

This study employs machine learning to estimate groundwater contamination vulnerability in Ibadan, southwestern Nigeria, using hydrogeophysical indicators from 353 Vertical Electrical Sounding (VES) surveys. A Random Forest classifier trained on GOD/GODT vulnerability labels achieved accuracy = 0.94, precision = 0.94, recall = 0.93, F1-score = 0.93, and AUC = 0.95. Overburden thickness and longitudinal conductance were the most significant predictors. The model identified fifteen high-vulnerability zones versus nine from conventional GODT, demonstrating its ability to capture nonlinear interactions that conventional methods miss.

Cite this Paper

BibTeX
@InProceedings{pmlr-v319-olukayode26a, title = {Machine Learning Prediction of Groundwater Contamination Vulnerability Using Hydrogeophysical Indicators in {Ibadan}}, author = {Olukayode, Oluwakemi Omolara and Folorunso, Sakinat and Bayewu, Olateju and Ojo, Odunayo and Olukayode, David and Omotola, Olubunmi and Sokan-Adeaga, Adewale and Olaseeni, Olayiwola}, booktitle = {Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments}, pages = {155--166}, year = {2026}, editor = {Folorunso, Sakinat and Ogundokun, Roseline and Oladipo, Francisca}, volume = {319}, series = {Proceedings of Machine Learning Research}, month = {11--14 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v319/main/assets/olukayode26a/olukayode26a.pdf}, url = {https://proceedings.mlr.press/v319/olukayode26a.html}, abstract = {This study employs machine learning to estimate groundwater contamination vulnerability in Ibadan, southwestern Nigeria, using hydrogeophysical indicators from 353 Vertical Electrical Sounding (VES) surveys. A Random Forest classifier trained on GOD/GODT vulnerability labels achieved accuracy = 0.94, precision = 0.94, recall = 0.93, F1-score = 0.93, and AUC = 0.95. Overburden thickness and longitudinal conductance were the most significant predictors. The model identified fifteen high-vulnerability zones versus nine from conventional GODT, demonstrating its ability to capture nonlinear interactions that conventional methods miss.} }
Endnote
%0 Conference Paper %T Machine Learning Prediction of Groundwater Contamination Vulnerability Using Hydrogeophysical Indicators in Ibadan %A Oluwakemi Omolara Olukayode %A Sakinat Folorunso %A Olateju Bayewu %A Odunayo Ojo %A David Olukayode %A Olubunmi Omotola %A Adewale Sokan-Adeaga %A Olayiwola Olaseeni %B Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments %C Proceedings of Machine Learning Research %D 2026 %E Sakinat Folorunso %E Roseline Ogundokun %E Francisca Oladipo %F pmlr-v319-olukayode26a %I PMLR %P 155--166 %U https://proceedings.mlr.press/v319/olukayode26a.html %V 319 %X This study employs machine learning to estimate groundwater contamination vulnerability in Ibadan, southwestern Nigeria, using hydrogeophysical indicators from 353 Vertical Electrical Sounding (VES) surveys. A Random Forest classifier trained on GOD/GODT vulnerability labels achieved accuracy = 0.94, precision = 0.94, recall = 0.93, F1-score = 0.93, and AUC = 0.95. Overburden thickness and longitudinal conductance were the most significant predictors. The model identified fifteen high-vulnerability zones versus nine from conventional GODT, demonstrating its ability to capture nonlinear interactions that conventional methods miss.
APA
Olukayode, O.O., Folorunso, S., Bayewu, O., Ojo, O., Olukayode, D., Omotola, O., Sokan-Adeaga, A. & Olaseeni, O.. (2026). Machine Learning Prediction of Groundwater Contamination Vulnerability Using Hydrogeophysical Indicators in Ibadan. Proceedings of IndabaX Nigeria 2026: Building Scalable AI That Works: From Research to Deployment in Resource-Constrained Environments, in Proceedings of Machine Learning Research 319:155-166 Available from https://proceedings.mlr.press/v319/olukayode26a.html.

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