Slime Mold Algorithm: a novel approach for estimating total petroleum hydrocarbons in soil

Estimating the pollution trend of biological resources such as soil is an important factor in environmental management. The present descriptive-applied research aimed to model and predict the amounts of total petroleum hydrocarbons (TPHs) in the soil of Ahvaz Operation Unit 1 using the Slime Mold Algorithm (SMA) in 2024. Data from the period 2012 to 2021 were analyzed by collecting 30 soil samples using a systematic grid sampling method. TPHs, including aliphatic and polycyclic aromatic hydrocarbons (PAHs), were measured by gas chromatography (GC). Ten-year climate data were used to increase prediction accuracy. The steps of implementing the SMA were defined using mathematical optimization terminology, including adaptive exploration, exploitation of the search space, and convergence. The findings showed that the average of the highest amount of PAH compounds was 2980.5 μg/kg, with the highest contamination reported for Chr and B.a.a (5542.5 and 3684.3 μg/kg, respectively). Also, the average of total aliphatic compounds was 3649 mg/kg. There was a statistically significant spatial difference between the concentrations among different sampling stations (p < 0.05). Based on the normalized dataset, the SMA model showed that the RMSE for aromatics and aliphatics were 0.135 and 0.148, indicating higher accuracy in predicting aromatic compounds. The coefficient of determination (R²) and adjusted were 0.72 and 0.68 for aromatics, and 0.65 and 0.60 for aliphatics, respectively. The results indicate that the SMA shows strong potential for wider use in modeling soil contamination and offers a practical framework for environmental pollution management, although further validation across different climatic regions is recommended.