Application of a simple genetic algorithm for the calibration of aquatic ecosystem model of an agricultural pond

Abstract

In this study, we aim to construct and apply a simple genetic algorithm (SGA) to optimize a large number of parameters of an one-box ecosystem model. The ecosystem model was used to simulate the water quality over a 6-month period based on the new observation data in an agricultural pond which was strongly influenced by a green algal bloom. Of the 54 parameters in this model, 10 important parameters were initially selected for the optimization, with one other parameter being subsequently added. The SGA program was used for three purposes, namely (1) to narrow the search space for the 10 parameters, (2) to assess the influence of the additional parameter on the optimization results, and (3) to observe the distribution and convergence of the optimized values for the 10 selected parameters. In the next step, new ranges for these 10 important parameters were assigned and the SGA was applied to all 54 model parameters to seek the optimum value for each parameter. The modeling approach and the results presented here provide valuable and reliable evidences of the optimum parameters for further simulations to clarify the mechanisms of the biochemical processes in the water.