An automatic hyperparameter optimization DNN model for precipitation prediction
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 u...
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| Vydané v: | Applied intelligence (Dordrecht, Netherlands) Ročník 52; číslo 3; s. 2703 - 2719 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.02.2022
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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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 optimize 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. |
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| AbstractList | 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 optimize 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. |
| Author | Peng, Yuzhong Gong, Daoqing Zhang, Hao Cai, Hongguo Deng, Chuyan Li, Hongya |
| Author_xml | – sequence: 1 givenname: Yuzhong surname: Peng fullname: Peng, Yuzhong email: jedison@163.com organization: School of Computer & Information Engineering, Nanning Normal University, College of Computer Science & Technology, Fudan University – sequence: 2 givenname: Daoqing orcidid: 0000-0002-0977-5211 surname: Gong fullname: Gong, Daoqing organization: School of Computer & Information Engineering, Nanning Normal University – sequence: 3 givenname: Chuyan surname: Deng fullname: Deng, Chuyan organization: School of Computer & Information Engineering, Nanning Normal University – sequence: 4 givenname: Hongya surname: Li fullname: Li, Hongya organization: Department of Science, Shangqiu University Applied Science and Technology College – sequence: 5 givenname: Hongguo surname: Cai fullname: Cai, Hongguo organization: Department of Mathematics and Computer Science, The Guangxi College of Education – sequence: 6 givenname: Hao surname: Zhang fullname: Zhang, Hao email: haoz15@fudan.edu.cn organization: College of Computer Science & Technology, Fudan University |
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| Keywords | Neural structure optimization Hyperparameter optimization Deep neural networks Neural architecture search Precipitation prediction Gene expression programming |
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| SubjectTerms | Artificial Intelligence Artificial neural networks Back propagation Back propagation networks Computer Science Gene expression Genetic algorithms Machines Manufacturing Mechanical Engineering Optimization Precipitation Processes Rainfall Searching Support vector machines |
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| Title | An automatic hyperparameter optimization DNN model for precipitation prediction |
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