Random forest and nature-inspired algorithms for mapping groundwater nitrate concentration in a coastal multi-layer aquifer system

Accurate prediction of groundwater nitrate concentrations is critical for pollution control and sustainable groundwater resource management. However, Ant optimization (Ant), Firefly, Multiple Objective Evolutionary (MOE), and Grey Wolf Optimization (GWO), are novel data-driven hydrid algorithms that are barely used in groundwater pollution studies. Yet, areas susceptible to groundwater nitrate concentration have not been evaluated in the Mekong Delta. In this paper, we develop five novel hybrid algorithms: Ant, Firefly, MOE, GWO and Particle Swarm Optimization (PSO) which are integrated with the random forest (RF) algorithm for mapping groundwater nitrate concentrations in the coastal multi-aquifers of the Mekong Delta. The geographic information system (GIS) environment was used to construct 24 conditional factors believed to affect groundwater nitrate contamination. In 216 wells, nitrate concentrations were collected and quantified, and the results were utilized as a dependent parameter in modeling. To assess the effectiveness of these proposed hybrid models, evaluation measures such as statistical error indices, confusion matrices, Taylor diagram, the probability density function of error, and scatter plots were used. The RF-Firefly model (RMSE = 6.25, AUC = 0.961) performed the best based on testing datasets, followed by the RF-MOE (RMSE = 8.67, AUC = 0.878), the RF-Ant (RMSE = 7.27, AUC = 0.874), the RF-GWO ( RMSE = 7.35, AUC = 0.87), RF-PSO (RMSE = 10.24, AUC = 0.864) and RF ( RMSE = 15.85, AUC = 0.816) models. The results of mapping groundwater nitrate concentrations also showed that the south-central region had a very high nitrate concentration (> 45 mg/L) compared to other places. It was also found that extensive agricultural activities, wells near farming areas, and overexploitation of groundwater were the main causes of nitrate pollution in the study. The proposed models show their capability and application in predicting groundwater nitrate pollution in various deltaic locations.