Research on habitat quality assessment and decision-making based on Semi-supervised Ensemble Learning method—Daxia River Basin, China

Abstract

Habitat quality is an indicator of the ecological evolution of a region, and evaluating habitat quality utilizing Machine Learning approaches can reflect the ecological status of a region more objectively. Gridded statistical ecological factors in the study region were utilized to build the Ecological Information State Layer (EISL) in the grid space. Calculate the Environmental Quality Index (EQI) of the sampled grid to evaluate the performance index of the models on the sampled grid. The model with the optimal performance index is selected for the task of habitat quality classification in the research region, and then especially decision-making advice is offered by inverting the degree of influence of ecological elements on habitat quality. The results show that: (1) The Semi-supervised Ensemble Learning model (Tri-training) Accuracy, Kappa coefficient, and F1-score are 0.93, 0.89, and 0.92, respectively, as the optimal model for the habitat quality classification task. (2) Based on the results of the ecological categories calculation, the current ecosystem quality of the southwestern section of the Daxia River Basin is maintained as a positive indicator. the ecosystem quality inside the living space centered on the cities of Linxia and Hezuo shows negative indicator changes. (3) The calculation results of the inverse importance of ecological factors show that vegetation index and human population density are the key factors impacting the habitat quality of the Daxia River Basin. In the ecological management of Daxia River Basin, the degree of spatial aggregation of people's settlements should be reduced while preserving and expanding vegetation cover. Using Tri-training's comprehensive analysis of multiple ecological factors, regional habitat quality can be accurately assessed.