Abstract

Environmental contamination due to industrial effluents, agricultural runoff, and urbanization has become a critical global concern. Accurate prediction of pollutant levels and assessment of biological remediation potential are essential for sustainable environmental management and public health protection. Traditional modeling approaches often struggle with complex, nonlinear interactions between contaminants and biological remediation agents. Conventional computational models frequently exhibit limitations in capturing the dynamic and stochastic nature of environmental systems. Moreover, existing prediction techniques may fail to optimize bioremediation strategies effectively, leading to inefficiencies in pollutant removal and prolonged environmental recovery times. In this study, we propose a hybrid soft computing framework integrating a novel meta-heuristic optimization algorithm with fuzzy logic and artificial neural networks. The meta-heuristic component efficiently tunes the parameters of the predictive models, while the fuzzy logic handles uncertainties inherent in environmental data. The framework was trained and validated using multi-source datasets comprising heavy metals, organic pollutants, and microbial remediation efficiency metrics. Comparative analysis with conventional machine learning models and standalone soft computing techniques was conducted to evaluate predictive accuracy and optimization performance. The proposed hybrid model showd superior predictive performance, achieving a mean absolute error (MAE) reduction of 18–25% compared to traditional models. Biological remediation efficiency predictions exhibited a 92% correlation with experimental observations, outperforming standalone neural networks and fuzzy inference models by 12–15%. The meta-heuristic optimization successfully identified optimal remediation strategies, reducing predicted contaminant levels by up to 35% under simulated intervention scenarios.

Authors

K. Padmapriya
R.M.D Engineering College, India

Keywords

Environmental Contaminants, Bioremediation, Hybrid Soft Computing, Meta-Heuristic Optimization, Predictive Modeling