使用地理加权集成学习生成网格化中国国内生产总值数据

Zekun Xu, Yu Wang, Guihou Sun, Yuehong Chen, Qiang Ma, Xiaoxiang Zhang
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引用次数: 2

摘要

网格化的国内生产总值(GDP)数据是许多地球科学应用的关键地表参数。最近,机器学习方法已经成为生成网格化GDP数据的强大工具。然而,大多数用于网格化GDP估计的机器学习方法很少考虑输入变量的地理属性。因此,在本研究中,开发了一种地理加权叠加集成学习方法来生成网格化GDP数据。采用随机森林、XGBoost和lightgbm三种算法作为基础模型,用地理加权回归代替叠加集成学习中的线性回归,局部融合三种预测。在中国进行了一个案例研究,以证明所提出方法的有效性。结果表明,本文提出的GDP降尺度方法优于三种基本模型和传统的叠加集成学习。同时,对县域GDP检验数据具有较好的预测能力,第一、第二、第三产业的R2分别为0.894、0.976、0.976。用镇一级GDP数据评价1公里网格化GDP数据预测精度较高(R2 = 0.787)。因此,所提出的GDP缩减方法为生成网格化GDP数据提供了一个有价值的选择。生成的中国2020年1公里网格化GDP数据对其他应用具有重要意义。
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Generating Gridded Gross Domestic Product Data for China Using Geographically Weighted Ensemble Learning
Gridded gross domestic product (GDP) data are a crucial land surface parameter for many geoscience applications. Recently, machine learning approaches have become powerful tools in generating gridded GDP data. However, most machine learning approaches for gridded GDP estimation seldom consider the geographical properties of input variables. Therefore, in this study, a geographically weighted stacking ensemble learning approach was developed to generate gridded GDP data. Three algorithms—random forest, XGBoost, and LightGBM—were used as base models, and the linear regression in stacking ensemble learning was replaced by geographically weighted regression to locally fuse the three predictions. A case study was conducted in China to demonstrate the effectiveness of the proposed approach. The results showed that the proposed GDP downscaling approach outperformed the three base models and traditional stacking ensemble learning. Meanwhile, it had good predictive power on county-level GDP test data with R2 of 0.894, 0.976, and 0.976 for the primary, secondary, and tertiary sectors, respectively. Moreover, the predicted 1 km gridded GDP data had a high accuracy (R2 = 0.787) when evaluated by town-level GDP data. Hence, the proposed GDP downscaling approach provides a valuable option for generating gridded GDP data. The generated 1 km gridded GDP data of China from 2020 are of great significance for other applications.
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