Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains
文献类型：期刊论文
| 作者 | Sharghi, Elnaz2,3,4; Nourani, Vahid1,2,3,4; Zhang, Yongqiang2; Ghaneei, Parnian3,4 |
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2022-07-01 |
| 卷号 | 610页码:16 |
| 关键词 | Spatial clustering Cluster ensemble Multi-step-ahead forecasting Model ensemble Ghorveh-Dehgolan plain (GDP) |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2022.127984 |
| 通讯作者 | Sharghi, Elnaz(sharghi@tabrizu.ac.ir) |
| 英文摘要 | In this study, first to identify the patterns of groundwater level (GWL) over the Ghorveh-Dehgolan plain (GDP) located in western Iran, as a data pre-processing scheme, three different types of clustering algorithms were applied to monthly GWL data sets of the piezometers. Then, the best structures of all clustering methods were integrated by Combining Multiple Clusterings via Similarity Graph (COMUSA) method to obtain the most homogenous patterns of GWL. The final results of the clustering step indicated that applying COMUSA could enhance the homogeneity of the clusters up to 25%. After dividing the GWL of GDP into four patterns, three single artificial intelligence (AI)-based models were applied to forecast multi-step-ahead GWL of centroid piezometer of each cluster. To benefit from the advantages of the single models, the outcomes were then combined with a neural averaging ensemble (NAE) technique as a post-processing step. Additionally, the assessment of the deep learning (DL) -based long-short-term memory (LSTM) application in multi-step ahead forecasting of GWL showed this method is not an appropriate choice compared to ensemble techniques for modeling the process with limited observed data. The comparison of the proposed GWL forecasting models of this study revealed the superiority of the NAE technique that enhanced the accuracy of the single models up to 23% in the testing phase. It could be concluded that the combination of cluster ensemble and model ensemble techniques could improve the performance of the individual method in reliable forecasting of the future GWL condition and the methodology of this study can be applied to the GWL of other plains. |
| WOS关键词 | ARTIFICIAL NEURAL-NETWORKS ; SYSTEM |
| 资助项目 | CAS-PIFI |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000833541200002 |
| 出版者 | ELSEVIER |
| 资助机构 | CAS-PIFI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/181819]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Sharghi, Elnaz |
| 作者单位 | 1.Near East Univ, Fac Civil & Environm Engn, Near East Blvd,Via Mersin 10, TR-99138 Nicosia, Turkey 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China 3.Univ Tabriz, Ctr Excellence Hydroinformat, 29 Bahman Ave, Tabriz, Iran 4.Univ Tabriz, Fac Civil Engn, 29 Bahman Ave, Tabriz, Iran |
| 推荐引用方式 GB/T 7714 | Sharghi, Elnaz,Nourani, Vahid,Zhang, Yongqiang,et al. Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains[J]. JOURNAL OF HYDROLOGY,2022,610:16. |
| APA | Sharghi, Elnaz,Nourani, Vahid,Zhang, Yongqiang,&Ghaneei, Parnian.(2022).Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains.JOURNAL OF HYDROLOGY,610,16. |
| MLA | Sharghi, Elnaz,et al."Conjunction of cluster ensemble-model ensemble techniques for spatiotemporal assessment of groundwater depletion in semi-arid plains".JOURNAL OF HYDROLOGY 610(2022):16. |
入库方式： OAI收割
来源：地理科学与资源研究所
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