Abstract
In vulnerability assessments, accurately determining the indicator weights is essential to ensure the results' precision and reliability. This paper proposes an optimized comprehensive symmetric Kullback-Leibler (K-L) distance weighting method, in which the comprehensive symmetric K-L distance for each indicator is calculated using a grid-based approach, and the normalized distance serves as the weight for each indicator. ArcGIS software was employed to assess the Ili River Basin flood vulnerability as a case study. The results reveal the following: (1) The grid-based optimized symmetric K-L distance method facilitated variable weight processing in the disaster assessments, where it offered a scientific and adaptable approach for indexing and weighting the Ili River Basin flood vulnerability, thus improving both the evaluation accuracy and practicality. (2) The spatial distribution of the Ili River Basin flood vulnerability levels was uneven, with higher vulnerability observed in the northwestern, southwestern, and southeastern regions, and lower vulnerability in the eastern and northeastern areas. Yining County, Yining City, and certain southern regions of Cocodala City were particularly vulnerable due to multiple influencing factors, including the population, economy, and society. These areas require focused attention and preventive measures.