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Fine-scale population mapping on Tibetan Plateau using the ensemble machine learning methods and multisource data

文献类型：期刊论文

作者Zhang, Huiming6; Fu, Jingqiao6; Li, Feixiang6; Chen, Qian6; Ye, Tao4,5; Zhang, Yili1,2,3; Yang, Xuchao6
刊名ECOLOGICAL INDICATORS
出版日期2024-09-01
卷号166页码:13
关键词Population spatialization Ensemble model Nighttime light Tibetan Plateau Location -based services data
ISSN号1470-160X
DOI10.1016/j.ecolind.2024.112307
英文摘要The Tibetan Plateau, known for its high elevation and sparse population distribution, heavily depends on gridded population data to enhance disaster prevention and management strategies. This study utilizes multi-source physical geographic and socio-economic factors to delineate the population distribution across the plateau. Using data from the seventh National Census in 2020, we apply three individual machine learning methods (Random Forest, GBDT, and XGBoost) and two multi-model ensemble methods (weighted average ensemble and stacking ensemble) to spatialize the population data into a 100-meter grid. The results reveal that the spatialization accuracy of all models exceeds that of the WorldPop dataset. Specifically, the Random Forest model (RMSE = 4061.09, nRMSE = 44.71 %) and the stacking ensemble model (RMSE = 4094.47, nRMSE = 44.26 %) demonstrate the highest accuracy among the individual and ensemble models, respectively. Emphasizing the importance of integrating multi-source big data, Tencent location-based services data emerges as a crucial variable across all models. This study highlights the effectiveness of ensemble models and multi-source big data in improving population mapping accuracy, especially in regions with complex terrains.
WOS关键词NIGHTTIME LIGHT ; LAND-COVER ; REGION ; DEGRADATION
资助项目Second Tibetan Plateau Scientific Expedition and Research program (STEP)[2019QZKK0603]
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001266388600001
出版者ELSEVIER
资助机构Second Tibetan Plateau Scientific Expedition and Research program (STEP)
源URL[http://ir.igsnrr.ac.cn/handle/311030/207715]  
专题陆地表层格局与模拟院重点实验室_外文论文
通讯作者Yang, Xuchao
作者单位1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China
2.CAS Ctr Excellence Tibetan Plateau Earth Sci, Beijing 100101, Peoples R China
3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
4.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China
5.Beijing Normal Univ, State Key Lab Earth Surface Proc & Resource Ecol E, Beijing 100875, Peoples R China
6.Zhejiang Univ, Ocean Coll, Zhoushan 316021, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Huiming,Fu, Jingqiao,Li, Feixiang,et al. Fine-scale population mapping on Tibetan Plateau using the ensemble machine learning methods and multisource data[J]. ECOLOGICAL INDICATORS,2024,166:13.
APA Zhang, Huiming.,Fu, Jingqiao.,Li, Feixiang.,Chen, Qian.,Ye, Tao.,...&Yang, Xuchao.(2024).Fine-scale population mapping on Tibetan Plateau using the ensemble machine learning methods and multisource data.ECOLOGICAL INDICATORS,166,13.
MLA Zhang, Huiming,et al."Fine-scale population mapping on Tibetan Plateau using the ensemble machine learning methods and multisource data".ECOLOGICAL INDICATORS 166(2024):13.

入库方式： OAI收割

来源：地理科学与资源研究所

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