Prediction of groundwater nitrate variations using AdaBoost approach

Nitrates have long been considered indicative of drinking water quality and a critical concern for human health. The evolution of advanced models for water quality management has spurred decision-makers to incorporate artificial intelligence technologies into water quality planning. This study aims to employ the AdaBoost model, one of the cutting-edge models in water quality management, to predict nitrate concentrations in groundwater using pH and EC (Electrical Conductivity) as input variables.

Initially, the study analyzed the Pearson correlation matrix and subsequently determined the input variables for multiple AdaBoost models with varying hyperparameters. A sensitivity and dependence analysis of the model's input variables was conducted to assess their impact on nitrate prediction.

The results obtained from the AdaBoost model reveal R-squared (R2) values of 0.915 for the training dataset and 0.924 for the test dataset. Additionally, the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) scores for the training dataset were recorded as 1.02, 1.01, 0.823, and 7.3%, respectively. For the test dataset, these metrics were observed in the order of 0.228, 0.477, 0.375, and 3.2%. The model's sensitivity analysis identified the pH variable as the most influential factor in nitrate prediction.

The model analysis demonstrates that the proposed method performs well in predicting nitrate concentrations. This approach holds significant potential for implementation as an intelligent system for forecasting water quality parameters.