An automatic hyperparameter optimization DNN model for precipitation prediction
Created by W.Langdon from
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- @Article{Peng:2022:AppliedIntelligence,
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author = "Yuzhong Peng and Daoqing Gong and Chuyan Deng and
Hongya Li and Hongguo Cai and Hao Zhang",
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title = "An automatic hyperparameter optimization {DNN} model
for precipitation prediction",
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journal = "Applied Intelligence",
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year = "2022",
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volume = "52",
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pages = "2703--2719",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Deep neural networks, ANN,
Precipitation prediction, Neural structure
optimization, Neural architecture search,
Hyperparameter optimization",
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ISSN = "1573-7497",
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DOI = "doi:10.1007/s10489-021-02507-y",
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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