DOI: 10.1016/j.gloplacha.2016.11.014
论文题名: Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine
作者: Ebrahimi H. ; Rajaee T.
刊名: Global and Planetary Change
ISSN: 0921-8181
出版年: 2017
卷: 148 起始页码: 181
结束页码: 191
语种: 英语
英文关键词: Groundwater level
; Iran
; Multi linear regression
; Support vector machine
; Time delay neural networks
; Wavelet
Scopus关键词: Groundwater
; Groundwater resources
; Linear networks
; Linear regression
; Neural networks
; Regression analysis
; Support vector machines
; Time delay
; Time series
; Wavelet analysis
; Decomposition level
; Groundwater level variation
; Iran
; Multi-linear regression
; Natural phenomena
; Support vector regression (SVR)
; Time delay neural networks
; Wavelet
; Wavelet decomposition
英文摘要: Simulation of groundwater level (GWL) fluctuations is an important task in management of groundwater resources. In this study, the effect of wavelet analysis on the training of the artificial neural network (ANN), multi linear regression (MLR) and support vector regression (SVR) approaches was investigated, and the ANN, MLR and SVR along with the wavelet-ANN (WNN), wavelet-MLR (WLR) and wavelet-SVR (WSVR) models were compared in simulating one-month-ahead of GWL. The only variable used to develop the models was the monthly GWL data recorded over a period of 11 years from two wells in the Qom plain, Iran. The results showed that decomposing GWL time series into several sub-time series, extremely improved the training of the models. For both wells 1 and 2, the Meyer and Db5 wavelets produced better results compared to the other wavelets; which indicated wavelet types had similar behavior in similar case studies. The optimal number of delays was 6 months, which seems to be due to natural phenomena. The best WNN model, using Meyer mother wavelet with two decomposition levels, simulated one-month-ahead with RMSE values being equal to 0.069 m and 0.154 m for wells 1 and 2, respectively. The RMSE values for the WLR model were 0.058 m and 0.111 m, and for WSVR model were 0.136 m and 0.060 m for wells 1 and 2, respectively. © 2016 Elsevier B.V. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006900528&doi=10.1016%2fj.gloplacha.2016.11.014&partnerID=40&md5=69e902353596dbd609156464dfaeba49
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/11780
Appears in Collections: 全球变化的国际研究计划 气候变化与战略
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作者单位: Department of Civil Engineering, University of Qom, Qom, Iran
Recommended Citation:
Ebrahimi H.,Rajaee T.. Simulation of groundwater level variations using wavelet combined with neural network, linear regression and support vector machine[J]. Global and Planetary Change,2017-01-01,148.