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研究夜间照明数据,PM2.5数据和城市GDP之间的关系和预测模型

Sen Chen¹, Junke Li¹

¹School of Information Engineering, Suqian University, Suqian, Jiangsu, China.

PeerJ. Computer science

|September 24, 2025

概括

这项研究引入了一种新模型,用于使用夜间灯光和颗粒物2.5 (PM2.5) 预测城市国内生产总值 (GDP). R&P-NLPG模型整合了这些因素,以实现更准确的经济预测.

科学领域:

环境科学环境科学
经济学经济学经济学
数据科学数据科学数据科学

背景情况:

工业化通过电力和夜间生产提高了生产率,但增加了空气污染 (PM2.5).
城市经济发展与能源消耗 (夜间照明) 和环境质量 (PM2.5) 密切相关.
现有的模型经常使用单个因素预测国内生产总值 (GDP),限制了准确性.

研究的目的:

通过结合夜间照明数据和PM2.5.5数据,提出一个预测城市GDP的综合模型.
通过利用多因素数据融合,提高GDP预测的准确性.

主要方法:

收集并预处理的夜间照明,PM2.5和GDP数据.
进行相关性分析以了解特征关系.
利用数据融合技术整合夜间照明和PM2.5数据.
构建了一个神经网络来建模融合特征与GDP之间的关系.

主要成果:

与单一特征模型相比,开发的R&P-NLPG模型显示出优异的预测性能.
综合方法显著提高了GDP预测的准确性.
该研究验证了将环境和能源消耗数据用于经济预测的有效性.

结论:

R&P-NLPG模型为城市GDP预测提供了更准确的方法.
整合夜间照明和PM2.5数据为经济驱动因素和环境影响提供了宝贵的见解.
这种方法可以帮助决策者平衡经济增长与环境可持续性.

关键词:

相关性分析是一项相关性分析.数据融合数据融合神经网络的神经网络的神经网络夜间照明灯的使用.对于PM2.5来说,这是一个很好的例子.城市GDP城市GDP

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