| DOI: | 10.1007/s11069-020-03892-2
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| 论文题名: | Optimized multi-output machine learning system for engineering informatics in assessing natural hazards |
| 作者: | Chou J.-S.; Truong D.-N.; Che Y.
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| 刊名: | Natural Hazards
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| ISSN: | 0921030X
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| 出版年: | 2020
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| 卷: | 101, 期:3 | | 起始页码: | 727
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| 结束页码: | 754
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| 语种: | 英语
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| 中文关键词: | Accelerated particle swarm optimization
; Computer-aided engineering informatics
; Least squares support vector regression
; Multi-output machine learning
; Natural hazards assessment
; System design and implementation
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| 英文关键词: | algorithm
; hazard assessment
; informatics
; machine learning
; natural hazard
; optimization
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| 英文摘要: | This work develops a novel metaheuristic optimization-based least squares support vector regression (LSSVR) model with a multi-output (MO) algorithm for assessing natural hazards. The MO algorithm is more efficient than the single-output algorithm because the relations among outputs can be estimated simultaneously by the proposed prediction model. Furthermore, the hyperparameters in MOLSSVR are optimized using an accelerated particle swarm optimization (APSO) algorithm combined with a self-tuning method to generate the best predictions and the fastest convergence. The APSO algorithm is validated by solving benchmark functions with unimodal and multimodal characteristics. The performance of APSO-MOLSSVR is compared with those of hybrid and single models yielded from standard multi-input single-output algorithms. A graphical user interface was designed as a stand-alone application to provide a user-friendly system for executing advanced data mining techniques. In real-world engineering cases, APSO-MOLSSVR achieved an error rate that was up to 63.55% better than those achieved using prediction models that are proposed in the single-output scheme. The system much more quickly and efficiently identified the optimal parameters and effectively solved multiple-output problems. © 2020, Springer Nature B.V. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/168433
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| Appears in Collections: | 气候变化与战略
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作者单位: | Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
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| Recommended Citation: | |
Chou J.-S.,Truong D.-N.,Che Y.. Optimized multi-output machine learning system for engineering informatics in assessing natural hazards[J]. Natural Hazards,2020-01-01,101(3)
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