An automatic hyperparameter optimization DNN model for precipitation prediction

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@Article{Peng:2022:AppliedIntelligence,

• author = "Yuzhong Peng and Daoqing Gong and Chuyan Deng and Hongya Li and Hongguo Cai and Hao Zhang",
• title = "An automatic hyperparameter optimization {DNN} model for precipitation prediction",
• journal = "Applied Intelligence",
• year = "2022",
• volume = "52",
• pages = "2703--2719",
• month = feb,
• keywords = "genetic algorithms, genetic programming, Gene expression programming, Deep neural networks, ANN, Precipitation prediction, Neural structure optimization, Neural architecture search, Hyperparameter optimization",
• ISSN = "1573-7497",
• DOI = "doi:10.1007/s10489-021-02507-y",
• abstract = "Deep neural networks (DNN) have gained remarkable success on many rainfall predictions tasks in recent years. However, the performance of DNN highly relies upon the hyperparameter setting. In order to design DNNs with the best performance, extensive expertise in both the DNN and the problem domain under investigation is required. But many DNN users have not met this requirement. Therefore, it is difficult for the users who have no extensive expertise in DNN to design optimal DNN architectures for their rainfall prediction problems that is to solve. In this paper, we proposed a novel automatic hyperparameters optimization method for DNN by using an improved Gene Expression Programming. The proposed method can automatically optimise the hyperparameters of DNN for precipitation modeling and prediction. Extensive experiments are conducted with three real precipitation datasets to verify the performance of the proposed algorithm in terms of four metrics, including MAE, MSE, RMSE, and R-Squared. The results show that: 1) the DNN optimized by the proposed method outperforms the existing precipitation prediction methods including Multiple Linear Regression (MLR), Back Propagation (BP), Support Vector Machine (SVM), Random Forest (RF) and DNN; 2) the proposed DNN hyperparameter optimization method outperforms state-of-the-art DNN hyperparameter optimization methods, including Genetic Algorithm, Bayes Search, Grid Search, Randomized Search, and Quasi Random Search.",

}

Genetic Programming entries for Yu-zhong Peng Daoqing Gong Chuyan Deng HongYa Li Hongguo Cai Hao Zhang

Citations