A decision-aid system for subway microenvironment health risk intervention based on backpropagation neural network and permutation feature importance method

Abstract

Subway has become an important commute mode in daily life. However, subway microenvironment poses a threat to people's health and there is growing concern about its health risks. In this paper, a decision-aid system for subway microenvironment health risk intervention was proposed based on the combination of backpropagation neural network algorithm and permutation feature importance method. The non-linear relationship between indicators and the risks of subway microenvironment was established using the neural network. On this basis, the implementation of stepwise intervention, neural network prediction, and the generation of strategies was achieved utilizing MATLAB programming. Moreover, a subway station in Nanjing was taken as an example to illustrate the implementation and verify the effectiveness of the system. With the help of the system, health risks in the subway microenvironment can be systematically analyzed, efficiently optimized, and gradually solved. Our research contributes to breaking the deadlock in subway microenvironment intervention, which has long been confined to predicting and summarizing strategies. It provides more objective and effective intervention strategies for the health risk management of subway microenvironments.