LEVY FLIGHT-ENHANCED SOFT COMPUTING APPROACHES FOR PREDICTING POLLUTION DYNAMICS AND OPTIMIZING BIOLOGICAL REMEDIATION UNDER CLIMATE CHANGE SCENARIOS

Climate change is intensifying environmental pollution, altering both pollutant distribution and the effectiveness of biological remediation strategies. Predicting pollution trends and designing adaptive remediation approaches are critical for sustainable ecosystem management. Traditional modeling techniques often struggle with the non-linear, multi-factorial nature of environmental systems. There is a pressing need for robust computational models that can accurately forecast pollution dynamics while optimizing biological remediation strategies under uncertain climate scenarios. Existing methods frequently face challenges in convergence speed, local optima avoidance, and adaptability to complex environmental datasets. This study introduces a Levy flight-enhanced soft computing framework, integrating recent meta-heuristic algorithms with fuzzy logic and neural computation. The approach leverages Levy flight-inspired exploration to improve global search capabilities, enabling better parameter tuning and predictive accuracy. Historical pollution datasets, climatic variables, and biological remediation performance indicators were used to train and validate the model. The framework evaluates the influence of temperature fluctuations, precipitation patterns, and pollutant load on remediation efficiency, providing actionable insights for environmental management. Experimental results demonstrate that the proposed Levy-based soft computing model achieves superior predictive accuracy, with a 15–20% improvement over conventional heuristic approaches in forecasting pollutant concentrations. Additionally, the framework identifies optimal biological remediation strategies, enhancing contaminant removal efficiency by up to 18% under varying climate scenarios. Sensitivity analysis highlights key climatic factors influencing remediation performance, confirming the model’s robustness and adaptability to dynamic environmental conditions.