Research on the Public's Support for Emergency Infrastructure Projects Based on K-Nearest Neighbors Machine Learning Algorithm

The public's support for emergency infrastructure projects, which will affect the government's credibility, social stability, and development, is very important. However, there are few systematic research findings on public support for emergency infrastructure projects. In order to explore the factors influencing the public's support and the degree of influence of each factor on the public's support, this paper employs K-Nearest Neighbors (KNN), a learning curve with m-fold cross-validation, grid search, and random forest to study the public's support for emergency infrastructure projects and its influencing factors. In this paper, a prediction model of the public's support for emergency infrastructure projects is developed based on KNN from data drawn from a questionnaire survey of 445 local residents concerning Wuhan Leishenshan Hospital, China. Two optimization algorithms, the learning curve with m-fold cross-validation and the grid search algorithm, are proposed to optimize the key parameters of the KNN predictive model. Additionally, quantitative analysis is conducted by using the random forest algorithm to assess the importance of various factors influencing public support. The results show that the prediction accuracy and model stability of the KNN prediction model based on the grid search algorithm are better than those using a learning curve with m-fold cross-validation. Furthermore, the random forest algorithm quantitative analysis shows that the most important factor influencing the public's support is government attention. The conclusions drawn from this paper provide a theoretical reference and practical guidance for decision making and the sustainable development of emergency infrastructure projects in China.