Resilience is a significant attribute used to measure the sustainable development of the environment, and research on optimal measurement models is very important. This study took 15 farms in the Jiansanjiang Administration of Heilongjiang Province in China as the research object and constructed a resilience evaluation indicator system containing 31 indicators for the regional agricultural soil-water resource composite system (ASWRS). The combined weight (BFCM-CRITIC) and entropy weight (EW) combined with the variable fuzzy assessment (VFA) model and the improved technique for order preference by similarity to an ideal solution (TOPSIS) model were used to calculate the resilience exponent and to analyse the characteristics of space-time variation. The stability and reliability of the two models under different weights were verified by the Spearman correlation coefficient and discrimination theory to determine the optimal resilience exponent diagnosis method. The results show that according to the BFCM-CRITIC-VFA model, the levels of resilience were the highest at the Nongjiang, Hongwei and Erdaohe farms, and the resilience levels were strong and scattered. The resilience of the Jiansanjiang Administration has been increasing over time, and the spatial distribution has generally decreased from north to south and from the Heilong River and Wusuli River basins inland. A comparison of the reliability and stability of the two models for different weights indicates that the VFA model optimized based on combined weights was superior to the other methods in terms of stability and reliability, which verifies that the BFCM-CRITIC-VFA model is the most suitable for measuring the resilience exponent.
Keywords: Agricultural soil-water resource complex system; BFCM-CRITIC-VFA; Jiansanjiang administration; Resilience; Space-time variation characteristics.
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