Hybrid ACO–ANN-Based Multi-objective Simulation–Optimization Model for Pollutant Load Control at Basin Scale

The systematic and integrative approach to optimum watershed management couples a watershed simulation model and an efficient optimization algorithm for evaluating great number of “what if” scenarios in the search domain. This study integrates a multi-objective Non-dominated Archiving Ant Colony Optimization (NA-ACO) algorithm as an optimization tool with Soil and Water Assessment Tool (SWAT) as the simulation module for optimum management of total suspended solids (TSS) loading to downstream water bodies. The resulting NA-ACO–SWAT model is computationally and experimentally expensive because of the large number of required function evaluations which demands repetitive execution of SWAT simulation model. To increase the computational efficiency of the watershed simulation model, the SWAT model is replaced by a trained artificial neural network (ANN) model to form a hybrid NA-ACO–SWAT–ANN model to efficiently develop the set of optimum non-dominated solutions for configuration and design of detention ponds in basin scale. The applicability of the proposed method is evaluated at Gharesou watershed in the northwest of Iran. The outcomes of the proposed approach is further analyzed and compared in terms of their quality of solutions and computational efficiencies. Results show that the proposed hybrid approach may reduce the computational time by 90 % while keeping the accuracy of the results in the same order.