Estimation of Economic Indicators using Residual Neural Network ResNet50 Spatially real and reliable of economic indicator is critical for both policy makers and researchers. The economic indicators are often used to reflect poverty in an area. At present, combining remote sensing imagery and machine learning are used as a new method for estimated economic indicator. In this paper, the deep residual network model ResNet50 is used to train remote sensing images to classify nighttime light intensity to obtains representative features, which are later used in ridge regression to fit to corresponding economic indicator. The Pearson coefficient between the predicted economic indicator and the actual economic indicator is calculated to evaluate the performance of the method. We estimated gross domestic product (GDP) and total retail sales of consumer goods (TRSCG) respectively, and the Pearson coefficients of GDP and TRSCG were both 0.85, which are higher than the results of linear regression model. The results show that the method has good prediction ability for GDP and TRSCG economic indicators. The satellite images in this paper are publicly available and therefore this method can be easily applied to other regions.

Deep Residual Learning for Nonlinear Regression Deep learning plays a key role in the recent developments of machine learning. This paper develops a deep residual neural network (ResNet) for the regression of nonlinear functions. Convolutional layers and pooling layers are replaced by fully connected layers in the residual block. To evaluate the new regression model, we train and test neural networks with different depths and widths on simulated data, and we find the optimal parameters. We perform multiple numerical tests of the optimal regression model on multiple simulated data, and the results show that the new regression model behaves well on simulated data. Comparisons are also made between the optimal residual regression and other linear as well as nonlinear approximation techniques, such as lasso regression, decision tree, and support vector machine. The optimal residual regression model has better approximation capacity compared to the other models. Finally, the residual regression is applied into the prediction of a relative humidity series in the real world. Our study indicates that the residual regression model is stable and applicable in practice.