Regional ecosystem health assessment using the GA-BPANN model: a case study of Yunnan Province, China

Abstract

ABSTRACT Background: Regional ecosystem health assessments are the basis for the sustainable development of society. However, an ecosystem is a complex integration of ecosystem mosaics and subsystems that influence each other, making it difficult to evaluate them using traditional assessment methods of linear and explicit functions. We introduce a back-propagation neural network model optimized by a genetic algorithm to evaluate ecosystem health in 16 districts in Yunnan Province. Result: (1) The model required fewer inputs to evaluate complex and nonlinear systems, avoided the need for subjective weights, and performed well in this practical application to regional ecosystem health assessment. (2) The ecosystem health in Yunnan Province was increasing, and there was a significant positive spatial autocorrelation during 2000–2020, showing that districts with high Ecosystem Health cluster together and the ecological protection policy of the region has produced a diffusion effect, leading to continuous improvement of the ecological health of the surrounding areas. High-low outlier areas of ecosystem health should be paid more attention, because of the increasing instability of local health levels. Conclusion: This study provides a methodological exploration for assessing spatial mosaics of different ecosystems at a regional scale.