Research on Airport Site Selection Method Based on Case Reasoning and Joint Analysis of Multiple Meteorological Elements

Meteorological conditions are a key factor affecting airport site selection and operational efficiency. To overcome the limitations of traditional methods in evaluating the joint impact of multiple meteorological elements, this paper aims to develop an airport site selection decision support method based on case-based reasoning (CBR) and multi-meteorological element clustering. Firstly, we propose a universal framework: utilizing K-means clustering to extract typical weather scenarios from historical meteorological data; subsequently, using Zhengzhou Xinzheng International Airport as a case study, a quantitative mapping relationship was established between these weather scenarios and flight operation efficiency (such as delay rate and cancellation rate) to calibrate and validate the model; finally, by calculating the frequency of occurrence of various weather scenarios at candidate sites, the future operational efficiency can be inferred, providing a ranking basis for site selection decisions. The results indicate that low-cloud-base weather has the greatest impact on flight takeoff performance, while good weather has a relatively small impact on flights. This method can effectively and quickly rank the advantages and disadvantages of all candidate airports, providing a reference for airport construction.