Identification of resilience characteristics of a regional agricultural water resources system based on index optimization and improved support vector machine

Abstract

Abstract In order to solve the gap and accuracy in the analytical methods of the resilience of a regional agricultural water resources system, a suitable evaluation index system based on the optimal index model was introduced and applied to the 15 farms in the Jiansanjiang Administration of Heilongjiang Province of China. An improved support vector machine (SVM) was used to analyze the resilience level of each farm for the selected time period. The test results showed that the indicator optimization model had the advantage of eliminating redundant indicators and ensuring the maximum content of screening indicators. The indicator system reflected all original information by 34% of initial indicators. The results showed that the particle swarm optimization-support vector machine (PSO-SVM) model had higher accuracy for the evaluation of agricultural water resource resilience through the analysis of stability and reliability of each model. The spatial pattern of resilience over selected farms was generally characterized by ‘low in the southwest and high in the northeast’. The research achievements may provide technical and theoretical support for solving problems of index optimization and analysis methods of system resilience, and have an important theoretical and practical significance for promoting the sustainable development of regional agricultural water resources systems.