基于NPP/VIIRS夜间灯光数据的GDP智能预测模型研究

针对现今传统统计方法难以准确及时的揭示经济参量时空信息且估算GDP精度不足的问题,本文利用国家极轨卫星携带的可见光和近红外成像辐射计（NPP/VIIRS）获取的2012-2018年夜间灯光影像为数据源，对数据产生的误差进行处理得到了可以用来定量分析的长时间序列数据；首先采用随机森林算法对数据集进行预测研究，在此基础上提出一种基于袋外数据估计的回归误差，采用改进的网格搜索算法（Grid Search）对随机森林模型进行参数调优。同时也使用贝叶斯优化（Bayesian Optimization）对随机森林(RF)模型进行参数调优。运用嵌套5F-CV，并通过外部5F-CV循环估算模型的泛化能力，内部5F-CV循环用于确定最佳参数找出最优参数模型，建立自动预测系统，根据输入的研究区数据，使算法模型自动进行精准预测。结果表明基于贝叶斯优化改进的随机森林算法在预测GDP时最好，预测精度达到97%，具有较高的准确率和鲁棒性。研究结果展示了机器学习算法和夜间光照指数用于在县级尺度上预测GDP表现出显著的能力。

Research on GDP Intelligence Forecasting Models Based on NPP/VIIRS Nighttime Iight Imagery

Nowadays, In view of the problem that it is difficult for traditional statistical methods to accurately and timely reveal the temporal and spatial information of economic parameters, and the accuracy of GDP estimation is insufficient. In this paper, the nighttime light images from 2012 to 2018 obtained by NPP/VIIRS are used as the data source to process the data errors and obtain the long time series data which can be used for quantitative analysis; Firstly, the random forest algorithm is used to predict the data set. On this basis, a regression error based on out of bag data estimation is proposed, and an improved grid search algorithm is used to optimize the parameters of the random forest model. At the same time, Bayesian optimization is used to optimize the parameters of random forest(RF) model. Nested 5f-cv is used to estimate the generalization ability of the model through the external 5f-cv cycle. The internal 5f-cv cycle is used to determine the optimal parameters, find out the optimal parameter model, establish the automatic prediction system, and make the algorithm model automatically and accurately predict according to the input data of the study area. The results show that the improved random forest algorithm based on Bayesian optimization is the best in GDP prediction, and the prediction accuracy reaches 97%, with high accuracy and robustness. The results show that the machine learning algorithm and nighttime illumination index can be used to predict GDP at county level.