首页|Southeast University Reports Findings in Machine Learning (Examining the rationality of Giant Panda National Park’s zoning designations and management measures for habitat conservation: Insights from interpretable machine learning methods)
Southeast University Reports Findings in Machine Learning (Examining the rationality of Giant Panda National Park’s zoning designations and management measures for habitat conservation: Insights from interpretable machine learning methods)
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New research on Machine Learning is the subject of a report. According to news reporting originating from Nanjing, People’s Republic of China, by NewsRx correspondents, research stated, “The examination of the rationality of zoning and management measures in the initial establishment of national parks in China is of great significance for supporting decision-making regarding habitat conservation. There exists a research gap in exploring the threshold effects of both environmental and human-related factors on habitats in the context of national parks.” Our news editors obtained a quote from the research from Southeast University, “However, it may be a challenge because of the limited species distribution data. Using the Sichuan region of the Giant Panda National Park (GPNP) as an example, this study made use of accessible remote sensing and big data to predict the distribution of giant panda habitat (GPH) in 2020 by constructing a species distribution model based on the random forest algorithm. Interpretable machine learning methods, namely Partial dependence plots (PDPs) and SHapley Additive exPlanations (SHAP), were utilized to uncover the underlying mechanisms of environmental and anthropogenic factors influencing the GPH distribution in Sichuan province. Through GIS overlay analysis, areas where conflicts between human settlements, transportation infrastructure, and GPH exist were identified. Our findings indicated a potential 28.44 % decrease in GPH from 2014 to 2020. Environmental factors such as temperature, topography, and vegetation type, as well as anthropogenic factors including distance to built-up areas and transportation infrastructure, notably distance to national roads, provincial roads and city arterial roads, influenced the GPH distribution with threshold effects significantly. The overlay analysis revealed escalated conflicts between human settlements, transportation infrastructure, and GPH in 2020 compared to 2014. Currently, the Sichuan region of the GPNP implements two zones: a core protection zone and a general control zone, covering 63.71 % of the GPH, while 36.29 % remains outside the management scope.”
NanjingPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningNational Parks
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