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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 13-27页

作者： Xuan Wang Yan Dong Jing Yang Zhipeng Liu Jinsuo Lu School of Environmental and Municipal Engineering Xi’an University of Architecture and TechnologyXi’an710055China Shaanxi Key Laboratory of Environmental Engineering Xi’an University of Architecture and TechnologyXi’an710055China State Key Laboratory of Green Building in West China Xi’an University of Architecture and TechnologyXi’an710055China

Neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data *** essential step in developing an NN is the hyperparameter *** practice,it is common to manually determine hyperparameters in the studies of NNs in water resources *** may result in considerable randomness and require significant computation time;therefore,hyperparameter optimization(HPO)is *** study adopted five representatives of the HPO techniques in the surface water quality prediction tasks,including the grid sampling(GS),random search(RS),genetic algorithm(GA),bayesian optimization(BO)based on the Gaussian process(GP),and the tree Parzen estimator(TPE).For the evaluation of these techniques,this study proposed a method:first,the optimal hyperparameter value sets achieved by GS were regarded as the benchmark;then,the other HPO techniques were evaluated and compared with the benchmark in convergence,optimization orientation,and consistency of the optimized *** results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence,reasonable optimization orientation,and the highest consistency rates with the benchmark *** optimization consistency rates via TPE for the hyperparameters hidden layers,hidden dimension,learning rate,and batch size were 86.7%,73.3%,73.3%,and 80.0%,*** the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test,the proposed benchmark-based HPO evaluation approach is feasible and robust.

关键词： Neural networks Hyperparameter optimization Surface water quality prediction bayes optimization Genetic algorithm

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