International Journal of Science, Engineering and TechnologyAuthors: Bijay Kumar Singh, Jasvir Singh
Abstract: Groundwater contamination presents a critical challenge to environmental sustainability and public health, particularly in regions facing rapid industrialization and agricultural intensification. Traditional analytical and statistical approaches often struggle to model the complexity, uncertainty, and nonlinearity inherent in subsurface pollution processes. This review explores the application of soft computing (SC) techniques—including Artificial Neural Networks (ANN), Fuzzy Logic (FL), Support Vector Machines (SVM), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and hybrid models—in groundwater pollution source identification and analysis. A systematic literature review (2010–2025) reveals that SC techniques effectively handle incomplete datasets, imprecise inputs, and non-linear contaminant transport. ANN and hybrid models exhibit high prediction accuracy for pollutant concentrations, while FL excels in qualitative risk mapping. SVM models perform well in binary classification of contaminated zones using limited data. GA and PSO are widely used for optimization tasks such as well placement and parameter calibration. Comparative analysis across global case studies highlights the strengths, limitations, and ideal applications of each technique. The study concludes that hybrid SC models offer the most robust performance for integrated risk mapping and multi-pollutant modeling. Future research should focus on explainable AI, transfer learning, and real-time sensor data integration to enhance model interpretability and deployment in decision-support systems.
DOI: http://doi.org/10.5281/zenodo.17748657
