Design a neuro-heuristic Morlet Wavelet neural network procedure for the reactive transport system

The present work shows the numerical performances through stochastic paradigms of the reactive transport system (RTS). The designed construction of the solver is presented through the Morlet wavelet function, which works as an artificial neural network and the hybridization of genetic algorithm and active set. An objective function is created using the differential RTS, which is optimized through the hybrid capability of the designed scheme. The precision of the solver is perceived in comparison with the obtained and reference outcomes, absolute error, and number of multiple executions based on different operators. The inspiration to exploit the Morlet wavelet neural network process and the hybridization methods comes to present precise and reliable solutions for solving the nonlinear and stiff differential systems. Three RTS cases are numerically treated to validate the efficiency of the designed process, and the performance of the scheme is also validated by using different statistical operators.