An adaptive neural network embedded genetic algorithm approach for inverse water quality modeling

This paper proposes a neural network (NN)-embedded genetic algorithm (GA) approach for solving inverse water quality modeling problems to overcome the computational bottleneck of inverse modeling by replacing a water quality model with an efficient NN functional evaluator. An existing one-step, NN-embedded GA approach is found incapable of solving an inverse water quality modeling problem because it tends to fail in guiding the global search process to converge toward the near optima. As a remedy, an adaptive NN-GA approach is proposed to achieve a gradual convergence toward the near optima through an iterative network learning method. The proposed approach is applied to a full-scale, numerical example, and the result shows that the adaptive NN-GA approach is capable of obtaining near-optimal solutions for the inverse problem of a complicated water quality model.

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