Groundwater Quality Assessment and Modeling Using Water Quality Index and Mathematical Models: Case Study of Bam Province, Burkina Faso

Abstract

In light of groundwater's fundamental role in providing drinking water to populations in the Sahel, its management and monitoring are vital for predicting and mitigating future crises. The goal of this study is to assess the groundwater quality in Bam province using the Water Quality Index (WQI), and to predict these indices through the application of Artificial Neural Networks (ANN) and traditional Multiple Linear Regression (MLR) techniques. In this context, a variety of physicochemical parameters such as Total Hardness (TH), pH, Electrical Conductivity (EC), calcium, magnesium, sodium, potassium, ammonium, bicarbonate, chloride, sulphate, nitrite, nitrate, phosphorus, and fluoride were collected from 154 boreholes, analysed, and used to calculate the WQIs. These parameters were employed as inputs, while the WQIs served as the output target for the models. The data were arranged in ascending order based on the indices, with 70% of the data reserved for training the models and 30% for testing. The groundwater quality in the study area, characterized by its geological heterogeneity and discontinuous fractures affecting groundwater flow, is predominantly excellent, with 95.45% of the samples having WQIs below 50. The remaining 4.55% is split between good (3.90%) and poor (0.65%) quality, with WQIs ranging from 50–100 and 100–200, respectively. In terms of predictive modeling, the ANN method provided the most accurate results, with R² (coefficient of correlation) = 0.99, RMSE (Root Mean Scare Error) = 0.0037, and MAE (Mean Absolute Error) = 0.0032 for the training set, and R² = 0.96, RMSE = 4.46, and MAE = 3.27 for the testing set. By comparison, the traditional method showed lower accuracy with R² = 0.61, RMSE = 2.71, MAE = 2.02 for the training set, and R² = 0.93, RMSE = 7.97, MAE = 6.32 for the testing set. The slight decrease in model accuracy during the testing phase is attributed to the challenge of modeling strong indices with weaker ones, as well as the geological heterogeneity and discontinuities that complicate groundwater quality prediction. However, this does not affect the model’s ability to predict extreme situations, such as water pollution events.