Sustainable Groundwater Exploration
AI-powered geophysical methods to locate clean water in data-scarce regions across Africa and beyond.
The Challenge: Finding Water Where Data Is Scarce
In rural Africa, failed water boreholes represent wasted resources and missed opportunities for communities in need. Traditional geophysical surveys are expensive and time-consuming. My research applies AI to AMT and CSAMT data to dramatically improve the probability of drilling success.
The Workflow
Open-Source Tools for a Global Challenge
Research impact requires accessible tools. I developed pyCSAMT and WATex — open-source Python packages that put state-of-the-art geophysical inversion and AI-assisted hydrogeology workflows in the hands of scientists and practitioners worldwide.
Open science is not just about sharing papers — it's about sharing the tools that make the science reproducible and applicable.
MADF — Detecting Toxic Leachate with AI
Beyond locating fresh groundwater, protecting it from contamination is equally critical. This line of work applies a Multifaceted Anomaly Detection Framework (MADF) to electrical resistivity tomography (ERT) data to map and delineate toxic leachate plumes escaping from failed landfill liners.
When Landfills Fail: Toxic Leachate in the Subsurface
Anti-seepage membrane failures in landfills release toxic lixiviate — a mix of organic contaminants, heavy metals, and pathogens — into the surrounding soil and groundwater. Traditional ERT inversion locates anomalies roughly, but cannot reliably delineate excavation boundaries with the precision remediation teams need.
Majority-Vote Funnel: Three Detectors, One Ground Truth
MADF runs three unsupervised anomaly detectors — Isolation Forest (IF), One-Class SVM (OC-SVM), and Local Outlier Factor (LOF) — on resistivity features extracted from ERT sections. A majority-vote funnel fuses their binary outputs into a single confirmed anomaly mask (B_MADF), suppressing false positives from any single detector while preserving true leachate signatures.
MADF majority-vote funnel: IF + OC-SVM + LOF → confirmed binary leachate truth (B_MADF)
From ERT Sections to Excavation Perimeter
Validated on two profiles (G1040 and G2030) at an active landfill site, MADF output achieved a Youden index of J = 0.095–0.052 — a substantial improvement over standard inversion (J = 0.027–0.046). The delineated leachate zone matched the confirmed membrane tear at 15 m depth, enabling engineers to define a precise excavation perimeter.
Traditional ERT inversion vs. MADF AI output on profiles G1040 and G2030
Confirmed leakage zone and recommended excavation perimeter derived from MADF
Key Research Outcomes
- 6+ peer-reviewed publications in leading hydrogeophysics and water resources journals.
- Drilling success rates above 90% demonstrated across field campaigns in Côte d'Ivoire.
- Released
watexandpyCSAMTas open-source Python packages with full documentation. - WATER4ALL for Africa initiative launched to scale AI-assisted groundwater exploration continent-wide.
Related publications
- A mixture learning strategy for predicting aquifer permeability coefficient KKouadio, K. L.; Liu, J.; Liu, W.; Liu, R. · Computers & Geosciences · 2025
- A novel approach for water reservoir mapping using controlled-source audio-frequency magnetotelluric in Xingning area, Hunan Province, ChinaKouadio, K. L.; Liu, R.; Malory, A. O.; Liu, W.; Liu, C. · Geophysical Prospecting · 2023
- Ensemble Learning Paradigms for Flow-Rate Prediction BoostingKouadio, K. L.; Liu, J.; Kouamelan, S. K.; Liu, R. · Water Resources Management · 2023
- watex: machine learning research in water explorationKouadio, K. L.; Liu, J.; Liu, R. · SoftwareX · 2023
- Groundwater Flow-Rate Prediction from Geo-Electrical Features using Support Vector MachinesKouadio, K. L.; Loukou, N. K.; Coulibaly, D.; Mi, B.; Kouamelan, S. K.; Gnoleba, S. P. D.; Zhang, H.; Xia, J. · Water Resources Research · 2022
- pyCSAMT: An alternative Python toolbox for groundwater exploration using controlled-source audio-frequency magnetotelluricKouadio, K. L.; Liu, R.; Mi, B.; Liu, C. · Journal of Applied Geophysics · 2022