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An expert-knowledge-based algorithm for time-varying multi-objective coastal groundwater optimization
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DOI：10.1016/j.jhydrol.2022.128396.png)
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Groundwater optimization models coupling Non-dominated Sorting Genetic Algorithm II (NSGAII) with surrogate models have shown great success in recent years. However, most previous models adopted global optimization over time steps while ignoring the time-varying properties of optimization objectives, which may lead to an unrealistic strategy for specific months. Moreover, employing global optimization for time-varying optimization may result in computation inefficient. To address these issues, We developed EGN combining Expert knowledge, Graph Attention Networks and the NSGAII algorithm. Specifically, EGN defines a deterministic policy based on the encoded Expert knowledge to efficiently select the optimal strategy from the Pareto Front given by NSGAII. The framework significantly reduced the asymptotic complexity from O(N-T) (global optimization) to O(N-2). We evaluate EGN on a real-world dataset and the results show that EGN outperforms baselines and global optimization.
Keyword:
Groundwater management
Graph attention network
Deep learning
NSGAII
Seawater intrusion
期刊
IF:
6.3
论文数:
2.4W
被引数:
9.8W
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