Hybrid Modeling of Deep Neural Networks and Ordinary Logistic Regression for Predicting Groundwater Quality

Groundwater quality plays a crucial role in daily life and public health, particularly in regions that rely on groundwater resources for domestic, agricultural, and industrial needs. Predictive modeling of groundwater quality is essential for managing and protecting water resources sustainably. This study proposes a hybrid approach combining deep neural networks (DNNs) and ordinary logistic regression (OLR) to enhance the accuracy of groundwater quality predictions. This hybrid approach leverages the capability of DNNs to capture complex non-linear patterns, while OLR is utilized for simpler and more structured coefficient interpretation of factors influencing water quality. The data used in the study includes various environmental and hydrogeological variables affecting groundwater quality, such as pH, heavy metal content, and other minerals. The results indicate that the hybrid DNN-OLR model achieves higher predictive accuracy, at 92.23%, compared to using DNN or OLR individually, which yield accuracies of 58.80%. The integration of these two methods also offers advantages in result interpretation, with OLR providing more transparent insights into the influence of independent variables, while DNN delivers stronger predictive capabilities through non-linear data processing. Therefore, this hybrid model has the potential to be applied for real-time groundwater quality monitoring and as a decision-support tool in water resource management.