Application of Teaching-Learning-based Optimization algorithm for appraising solute transport parameters of mobile-immobile model

This study presents the first attempt to estimate solute transport parameters of mobile-immobile (MIM) model using Teaching-Learning-based Optimization (TLBO) algorithm. The developed inverse model was called TLBO-MIM inverse model and tested for conservative solute transport in a highly heterogeneous long soil column and reactive solute transport in a short column filled with Glendale clay loam soil. The MIM model simulated the observed breakthrough curves (BTCs) of the conservative solute at near (100 cm) and far away (900 cm, 1000 cm, and 1200 cm) downgradient observation points of long column very well, based on the parameters estimated using the TLBO-MIM inverse model. The simulations of the BTCs of the conservative and reactive solutes in the short column of 30 cm in length further demonstrated the capabilities of the developed inverse model. Also, various statistical indicators showed the robust performance of the TLBO-MIM inverse model in estimating the solute transport parameters of the MIM model in the heterogeneous porous media. Overall, the findings from this study demonstrated that the inverse model based on the TLBO algorithm fits the MIM model well with the experimental BTCs of the conservative and reactive solutes in the heterogeneous porous media. The ability of the TLBO-MIM inverse model to maintain a high level of accuracy with a minimal error across multiple runs highlights its stability and effectiveness. Unlike many metaheuristic-based approaches, the TLBO-MIM model does not require fine-tuning algorithm-specific parameters, making it more user-friendly and efficient.