Flood risk assessment of subway stations based on projection pursuit model optimized by whale algorithm: A case study of Changzhou, China

With rapid modernization, traffic jams have become a part of people's daily lives. An increasing number of cities have begun to build subways to solve this problem. This study focuses on the flood risk analysis of subway stations, which is often overlooked. However, such disasters cause serious mass casualties and huge property losses. Therefore, this is of great concern. This study applies the method of projection pursuit model optimized by whale algorithm to evaluate the flood disasters of subway stations. The results showed that the flood risk level of 11 subway stations was very low, that of 21 subway stations was low, that of seven subway stations was moderate, and that of one subway station was high. Compared to the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, the application of the projection pursuit model based on the whale algorithm has higher precision and better global searchability. The main contribution of this study is to provide a new method and idea for the field of flood risk assessment of subway stations, and to provide a scientific model and reference for local rail companies. This study used the whale algorithm to optimize the projection pursuit model and applied it to the flood risk assessment of subway stations. This is an innovation in this field that has strong engineering application significance.

Automated summary

This study focuses on the flood risk analysis of subway stations, using the projection pursuit model optimized by whale algorithm. Compared to the TOPSIS method, this approach achieves higher precision and better global searchability. The study provides a new method and idea for flood risk assessment and has significant engineering application value.