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A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning

文献类型：期刊论文

作者Xu, Zhihao1,2,4; Lv, Zhiqiang1,2,3; Li, Jianbo1,2; Shi, Anshuo1,2
刊名WATER RESOURCES MANAGEMENT
出版日期2022-07-29
页码20
ISSN号0920-4741
关键词Multifarious factors Time series Base learner Local extreme values Volatility
DOI10.1007/s11269-022-03255-5
英文摘要Predicting urban water demand is important in rationalizing water allocation and building smart cities. Influenced by multifarious factors, water demand is with high-frequency noise and complex patterns. It is difficult for a single learner to predict the nonlinear water demand time series. Therefore, ensemble learning is introduced in this work to predict water demand. A model (Word-embedded Temporal Feature Network, WE-TFN) for predicting water demand influenced by complex factors is proposed as a base learner. Besides, the seasonal time series model and the Principal Component Analysis and Temporal Convolutional Network (PCA-TCN) are combined with WE-TFN for ensemble learning. Based on the water demand data set provided by the Shenzhen Open Data Innovation Contest (SODIC), WE-TFN is compared with some typical models. The experimental results show that WE-TFN performs well in fitting local extreme values and predicting volatility. The ensemble learning method declines by approximately 68.73% on average on the Root Mean Square Error (RMSE) compared with a single base learner. Overall, WE-TFN and the ensemble learning method outperform baselines and perform well in water demand prediction.
资助项目National Key Research and Development Plan Key Special Projects[2018YFB2100303] ; Shandong Province colleges and universities youth innovation technology plan innovation team project[2020KJN011] ; Shandong Provincial Natural Science Foundation[ZR2020MF060] ; Program for Innovative Postdoctoral Talents in Shandong Province[40618030001] ; National Natural Science Foundation of China[61802216] ; Postdoctoral Science Foundation of China[2018M642613]
WOS研究方向Engineering ; Water Resources
语种英语
出版者SPRINGER
WOS记录号WOS:000832823300001
源URL[http://119.78.100.204/handle/2XEOYT63/19502]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Jianbo
作者单位1.Qingdao Univ, Coll Comp Sci & Technol, Qingdao 266071, Shandong, Peoples R China
2.Qingdao Univ, Inst Ubiquitous Networks & Urban Comp, Qingdao 266070, Shandong, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Qingdao Inst Bioenergy & Bioproc Technol, Qingdao 266101, Shandong, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Zhihao,Lv, Zhiqiang,Li, Jianbo,et al. A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning[J]. WATER RESOURCES MANAGEMENT,2022:20.
APA Xu, Zhihao,Lv, Zhiqiang,Li, Jianbo,&Shi, Anshuo.(2022).A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning.WATER RESOURCES MANAGEMENT,20.
MLA Xu, Zhihao,et al."A Novel Approach for Predicting Water Demand with Complex Patterns Based on Ensemble Learning".WATER RESOURCES MANAGEMENT (2022):20.

入库方式： OAI收割

来源：计算技术研究所

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