Multi objective calibration of large scaled water quality model using a hybrid particle swarm optimization and neural network algorithm


Large scaled simulation models, especially the water quality simulation models, are so complicated that makes calibration processes huge tasks; in order to attain optimum solution, lots of parameters must be calibrated, simultaneously. Methods based on evolutionary algorithm developed new horizons in calibration procedure. Hybrid algorithms are of the newest. In hybrid algorithms, one of the modules is applied as a simulator and the other one takes role as an optimization module. In this article, overcoming these challenges, hybrid ANN-PSO algorithm is applied in calibration process of water quality model CE-QUAL-W2. Here, Particle Swarm Optimization (PSO) provides simulation (CE-QUAL-W2) model with sets of parameters to simulate model. Using these results, Neural Network (estimator) is trained. In the next step, simulator would be replaced with estimator and Artificial Neural Network (ANN) would estimate simulator's behavior in a way less time. The first goal is to calibrate thermal parameter; going forward through this process needs water surface elevation parameter to be calibrated, too. As a result, the proposed model will become multi-objective one, applied in Karkheh reservoir in Iran during 6 month simulation period. The proposed approach overcomes the high computational efforts required if a conventional calibration search technique was used, while retaining the quality of the final calibration results. Estimator (ANN) embedded in optimization algorithm (PSO) in calibration process, undoubtedly, reduced run time while the answers have reliable quality.