气候变化领域集成服务门户(CCPortal): Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine

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DOI	10.1007/s44196-024-00485-w
	Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine
	Fatovatikhah, Farnaz; Ahmedy, Ismail; Noor, Rafidah Md
发表日期	2024
ISSN	1875-6891
EISSN	1875-6883
起始页码	17
结束页码	1
卷号	17 期号:1
英文摘要	As climate change increases the risk of extreme rainfall events, concerns over flood management have also increased. To recover quickly from flood damage and prevent further consequential damage, flood waste prediction is of utmost importance. Therefore, developing a rapid and accurate prediction of flood waste generation is important in order to reduce disaster. Several approaches of flood waste classification have been proposed by various researchers, however only a few focus on prediction of flood waste. In this study, a Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) approach is adapted to address these challenges. Two different raw datasets were obtained from the Advancing Sustainable Materials Management: Facts and Figures 2015 source. The datasets were for 9 years (1960, 1970, 1980, 1990, 2000, 2005, 2010, 2014, 2015), and are labelled as the materials generated in the Municipal Waste Stream from 1960 to 2015 and the materials Recycled and Composted in Municipal Solid Waste from 1960 to 2015. The waste types were grouped as paper and paperboard (PP), glass (GI), metals (Mt), plastics (PI), rubber and leather (RL), textiles (Tt), wood (Wd), food (Fd), yard trimmings (YT) and miscellaneous inorganic wastes (IW).
英文关键词	Flood waste; Flood management; Long short-term memory; Prediction; Deep learning
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS记录号	WOS:001207717800001
来源期刊	INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/294056
作者单位	Universiti Malaya
推荐引用方式 GB/T 7714	Fatovatikhah, Farnaz,Ahmedy, Ismail,Noor, Rafidah Md. Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine[J],2024,17(1).
APA	Fatovatikhah, Farnaz,Ahmedy, Ismail,&Noor, Rafidah Md.(2024).Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine.INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS,17(1).
MLA	Fatovatikhah, Farnaz,et al."Waste Prediction Approach Using Hybrid Long Short-Term Memory with Support Vector Machine".INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 17.1(2024).