With the rapid growth of China's trade, the aggravation of environmental damage, research on trade growth and environmental damage forecasting issues is of great significance for the formulation and coordination of trade policies, environmental policies, and industrial policies. This article mainly studies the application of neural network in the prediction of compensation for ecological environment damage caused by trade. In this paper, the neural network model is applied to time series forecasting and panel data forecasting respectively, and the MATLAB tool is used to simulate the model, and the environmental damage compensation caused by trade is predicted. After L = 7613 iteration training, the total error value gradually decreases, and the given total error accuracy is 0.001. At this point, training is complete. After training, the BP neural network has been able to correctly predict these training samples. The prediction accuracy of this study reached 98%. This shows that the BP neural network model established in this paper not only meets the overall classification accuracy, but also has high accuracy in predicting the compensation of the ecological environment damage of each sample data. The research results show that the model is applied to the prediction of compensation for eco-environmental damage caused by trade. It has the advantages of fewer samples, high prediction accuracy, and simple calculation. It provides a new way to study the prediction of environmental damage caused by trade.

Ying Liu and Yetong Xi. Neural Network in the Forecast of Compensation for Ecological Environment Damage Caused by Trade. International Journal of Frontiers in Sociology (2021), Vol. 3, Issue 13: 77-88. https://doi.org/10.25236/IJFS.2021.031313.

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