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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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Vol. 52; no. 3; pp. 2703 - 2719
Main Authors: Peng, Yuzhong, Gong, Daoqing, Deng, Chuyan, Li, Hongya, Cai, Hongguo, Zhang, Hao
Format: Journal Article
Language:English
Published: New York Springer US 01.02.2022
Springer Nature B.V
Subjects:
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
Neural structure optimization
Hyperparameter optimization
Deep neural networks
Neural architecture search
Precipitation prediction
Gene expression programming
ISSN:0924-669X, 1573-7497
Online Access:Get full text
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Summary: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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ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02507-y