气候变化领域集成服务门户(CCPortal): Optimized multi-output machine learning system for engineering informatics in assessing natural hazards

CCPortal

DOI	10.1007/s11069-020-03892-2
	Optimized multi-output machine learning system for engineering informatics in assessing natural hazards
	Chou J.-S.; Truong D.-N.; Che Y.
发表日期	2020
ISSN	0921030X
起始页码	727
结束页码	754
卷号	101 期号:3
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.
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
英文关键词	algorithm; hazard assessment; informatics; machine learning; natural hazard; optimization
语种	英语
来源期刊	Natural Hazards
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/205634
作者单位	Department of Civil and Construction Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
推荐引用方式 GB/T 7714	Chou J.-S.,Truong D.-N.,Che Y.. Optimized multi-output machine learning system for engineering informatics in assessing natural hazards[J],2020,101(3).
APA	Chou J.-S.,Truong D.-N.,&Che Y..(2020).Optimized multi-output machine learning system for engineering informatics in assessing natural hazards.Natural Hazards,101(3).
MLA	Chou J.-S.,et al."Optimized multi-output machine learning system for engineering informatics in assessing natural hazards".Natural Hazards 101.3(2020).