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An ensemble spatial prediction method considering geospatial heterogeneity

文献类型：期刊论文

作者Cheng, Shifen3,4; Wang, Lizeng3,4; Wang, Peixiao3,4; Lu, Feng1,2,3,4
刊名INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
出版日期2024-05-29
卷号N/A
关键词Spatial prediction spatial inference spatial heterogeneity spatial data mining ensemble learning
DOI10.1080/13658816.2024.2358052
产权排序1
文献子类Article ; Early Access
英文摘要Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process.
WOS关键词REGRESSION ; MODELS
WOS研究方向Computer Science ; Geography ; Physical Geography ; Information Science & Library Science
WOS记录号WOS:001242044500001
出版者TAYLOR & FRANCIS LTD
源URL[http://ir.igsnrr.ac.cn/handle/311030/205354]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Lu, Feng
作者单位1.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing, Peoples R China
2.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Cheng, Shifen,Wang, Lizeng,Wang, Peixiao,et al. An ensemble spatial prediction method considering geospatial heterogeneity[J]. INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,2024,N/A.
APA Cheng, Shifen,Wang, Lizeng,Wang, Peixiao,&Lu, Feng.(2024).An ensemble spatial prediction method considering geospatial heterogeneity.INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE,N/A.
MLA Cheng, Shifen,et al."An ensemble spatial prediction method considering geospatial heterogeneity".INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE N/A(2024).

入库方式： OAI收割

来源：地理科学与资源研究所

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