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Red tide time series forecasting by combining ARIMA and deep belief network
Qin, Mengjiao1; Li, Zhihang2; Du, Zhenhong1,3
Source PublicationKNOWLEDGE-BASED SYSTEMS
2017-06-01
Volume125Pages:39-52
SubtypeArticle
AbstractThe red tide occurs frequently in recent years. The process of the growth, reproduction, extinction of the red tide algal has a complex nonlinear relationship with the environmental factors. The environmental factors have characteristics including time continuity and spatial heterogeneity. These characteristics make it arduous to forecast red tide. This paper mainly analyzes the related factors of the red tide disasters. Based on the strong forecasting ability of Autoregressive Integrated Moving Average (ARIMA) model and the powerful expression ability of Deep Belief Network (DBN) on nonlinear relationships, a hybrid model which combines ARIMA and DBN is proposed for red tide forecasting. The corresponding ARIMA model is built for each environmental factor in different coastal areas to describe the temporal correlation and spatial heterogeneity. The DBN serves to capture the complex nonlinear relationship between the environmental factors and the red tide biomass, and then realizes the warning of red tide in advance. Furthermore, Particle swarm optimization (PSO) is introduced to enhance the speed of model training. Finally, ship monitoring data collected in Zhoushan coastal area and Wenzhou coastal area during 2008-2014 is used as the experimental dataset. The proposed ARIMA-DBN model is applied to forecasting red tide. The experimental results demonstrate that the proposed method achieves a good forecast of red tide. (C) 2017 Published by Elsevier B.V.
KeywordRed Tide Forecasting Arima Dbn Pso Arima-dbn
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.knosys.2017.03.027
WOS KeywordHARMFUL ALGAL BLOOMS ; SUPPORT VECTOR MACHINES ; HYBRID ARIMA ; MODEL ; PREDICTION ; OPTIMIZATION ; REGRESSION ; RISK
Indexed BySCI
Language英语
Funding OrganizationPublic Science and Technology Research Funds' Projects(201305012 ; Fundamental Research Funds for the Central Universities from Ministry of Education of the People's Republic of China(2016XZZX004-02) ; 201505003)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000401220100004
Citation statistics
Cited Times:13[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15104
Collection智能感知与计算研究中心
Affiliation1.Zhejiang Univ, Sch Earth Sci, Hangzhou 310027, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Zhejiang Prov Key Lab Geog Informat Sci, Hangzhou 310028, Zhejiang, Peoples R China
Recommended Citation
GB/T 7714
Qin, Mengjiao,Li, Zhihang,Du, Zhenhong. Red tide time series forecasting by combining ARIMA and deep belief network[J]. KNOWLEDGE-BASED SYSTEMS,2017,125:39-52.
APA Qin, Mengjiao,Li, Zhihang,&Du, Zhenhong.(2017).Red tide time series forecasting by combining ARIMA and deep belief network.KNOWLEDGE-BASED SYSTEMS,125,39-52.
MLA Qin, Mengjiao,et al."Red tide time series forecasting by combining ARIMA and deep belief network".KNOWLEDGE-BASED SYSTEMS 125(2017):39-52.
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