Simple Regression Beats Neural Networks in Race to Protect Nigerian Aquifers

Groundwater is the invisible lifeline of southeastern Nigeria, and a new study from the region of Ibeator and its surrounding communities suggests that the tools used to predict how well local aquifers can defend themselves against contamination may need a rethink. In research published in BMC Environmental Science, a team led by Ayatu Ojonugwa Usman of AE-Federal University Ikwo combined artificial neural networks with multivariate linear regression to forecast the protective capacity of aquifers across an area of roughly 2,756 square kilometers straddling the border of Imo and Anambra States. The surprising headline result: the humble statistical regression model dramatically outperformed the brain-inspired neural network, achieving a coefficient of determination of 0.9775 compared with a meager 0.0869 for the ANN when predicting hydraulic conductivity.

The stakes could hardly be higher. Groundwater quantity within Ibeator is already insufficient for its growing population, and inadequate investigation has produced a string of failed boreholes. Without a clear picture of how thick the protective layers above an aquifer are, how deep the water sits, and how readily contaminants could migrate downward, planners are essentially drilling blind. The study set out to close that knowledge gap by pairing a classic geophysical field technique with modern data-driven modeling, offering a template that could be transferred to regions with similar geology.

The fieldwork rested on twelve vertical electrical soundings conducted with an ABEM Tetrameter SAS 1000 resistivity meter using the Schlumberger configuration, with current electrode spacing reaching 900 meters. This method injects current into the ground and measures the resulting voltage differences, allowing researchers to reconstruct how electrical resistivity changes with depth. Because the resistivity of a layer depends on its lithology, porosity, water content, and salinity, these soundings act as a non-invasive X-ray of the subsurface. Several soundings were deliberately placed near existing boreholes so the interpreted geoelectric sections could be checked against observed lithology, a validation step that boosted confidence when the predicted depth to the aquifer of 90 and 81 meters compared favorably with the 110 meters recorded in the geological log at Okorobi.

Interpretation of the soundings revealed a seven-layer subsurface model across the study area, with resistivity values spanning from about 72 ohm-meters in conductive clays to more than 8,000 ohm-meters in resistive sands. At one representative station, the sequence progressed from lateritic topsoil through clay, silty sand, dry sand, wet sand, and finally a thick saturated sandstone aquifer extending to depths of around 170 meters before a basal clay was encountered. From these geoelectric parameters, the team derived the Dar-Zarrouk quantities, longitudinal conductance and transverse resistance, which link the electrical behavior of the subsurface to its hydraulic properties through Darcy-type relationships. Those derived parameters, including hydraulic conductivity, transmissivity, and storativity, became the raw material for the machine learning comparison.

The spatial patterns that emerged are striking. Aquifer thickness is greatest in the eastern part of the study area around Umudime, reaching 90 to 210 meters, while the western communities of Okorobi, Okahia, and Uhuala sit above much shallower aquifers. Overall aquifer depths ranged from about 45.6 meters to 408.85 meters, averaging 227.21 meters. Resistivity of the aquifer material was highest, between 10,000 and 28,000 ohm-meters, around Okorobi and Uhuala, and lowest, between 1 and 2,000 ohm-meters, toward the east and northeast. Crucially, hydraulic conductivity, with values between roughly 0.000174 and 0.000215 meters per second, showed an inverse relationship with resistivity: where the subsurface resists electrical current, it also tends to resist the flow of water.

Transmissivity and storativity maps echoed the same geography, with transmissivity values from about 0.0035 to 0.0455 square meters per second and storativity from roughly 0.0077 to 0.324 square meters per second, both declining from north to south and lowest at the western and eastern edges. These variations matter because they translate directly into vulnerability. A shallow aquifer with thin protective cover and high transmissivity offers contaminants a fast track to the water table, whereas deeper, better-shielded units are naturally more resilient. The lithologic shifts observed from sand and sandstone in the northeast toward shale-rich sequences in the northwest, near the contact between the Imo Shale and the Benin Formation, further modulate how water moves and how well each zone is protected.

With the dataset assembled, the researchers built two families of predictive models. The artificial neural network used a feedforward, supervised architecture with backpropagation learning, taking three inputs, aquifer depth, aquifer resistivity, and aquifer thickness, through a hidden layer of three nodes to a single output. Seventy percent of the samples trained the network and thirty percent were reserved for testing, with the architecture tuned experimentally by adjusting neuron counts and training proportions. The multivariate linear regression approach, by contrast, fit explicit equations relating the same three predictors to each hydraulic property, with predictor selection guided by Pearson correlation coefficients and standard assumption checks for linearity, multicollinearity, homoscedasticity, normality of residuals, and independence of errors.

The head-to-head results were unambiguous. For hydraulic conductivity, the ANN managed an R-squared of only 0.0869, with training errors of 4.037 times ten to the minus five in root mean squared error and 0.343 in mean absolute percentage error, while the MLR achieved an R-squared of 0.9775 with far lower training errors. For transmissivity, the ANN reached an R-squared of 0.9157 against 0.9958 for the regression model, and for storativity the MLR again led with 0.9868 versus 0.9157. The authors attribute the neural network’s underperformance to the small dataset: ANNs typically require large volumes of data to learn complex nonlinear patterns, and with only twelve soundings the network likely underfit the underlying relationships, whereas the regression equations captured the dominant linear structure with remarkable precision.

Sensitivity analysis of the trained models also revealed which inputs mattered most, with aquifer depth and resistivity ranking highest in different orders depending on the target property. The practical payoff is a set of explicit equations that water managers in Ibeator and geologically similar terrain can apply to new geoelectric data to estimate hydraulic conductivity, transmissivity, and storativity without expensive pumping tests. That, in turn, enables targeted protection: communities over shallow, highly transmissive aquifers can be prioritized for stricter sanitation controls and careful borehole siting, while deeper, better-protected zones can absorb more intensive development.

The broader lesson resonates well beyond southeastern Nigeria. In an era when deep learning dominates headlines, this study is a reminder that model sophistication must match data availability, and that transparent statistical models can deliver both accuracy and interpretability where data are scarce. The authors suggest that the hybrid strategy, using neural networks to probe nonlinear structure and regression to quantify the influence of each factor, offers a holistic picture of aquifer vulnerability and a practical path toward sustainable groundwater management, improved water security, and environmental conservation for the region’s growing population.

Subject of Research: Prediction of aquifer protective capacity and hydraulic parameters in southeastern Nigeria using artificial neural networks and multivariate linear regression applied to vertical electrical sounding data

Article Title: Enhancing aquifer protective capacity prediction over Ibeator and environ, Southeastern Nigeria using artificial neural networks and multivariate linear regression analysis

Article References: Usman, A. O., Akakuru, O. C., Azuoko, G.-B., Abraham, E. M., Chinwuko, A. I., & Chizoba, C. J. (2024). Enhancing aquifer protective capacity prediction over Ibeator and environ, Southeastern Nigeria using artificial neural networks and multivariate linear regression analysis. BMC Environmental Science, 1(1), Article 13. https://doi.org/10.1186/s44329-024-00013-3

Image Credits: AI Generated

DOI: 10.1186/s44329-024-00013-3

Keywords: groundwater, aquifer vulnerability, artificial neural network, multivariate linear regression, vertical electrical sounding, hydrogeophysics, hydraulic conductivity, transmissivity, storativity, Nigeria, geoelectric survey, water resource management