Abstract
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.
Authors
K.S. Suresh
Rajeswari Vedachalam Government Arts College, India
Keywords
Levy Flight, Soft Computing, Pollution Prediction, Biological Remediation, Climate Change