基于XGBoost算法的亚热带地区生态旅游适宜性评价方法研究

Research on the Evaluation Method of Ecotourism Suitability in Subtropical Regions Based on XGBoost Algorithm

近年来,随着公众环境保护意识的提升,追求人与自然和谐共生的生态旅游方式越来越受到人们的普遍关注。面对丰富多样的生态旅游资源及国内生态旅游的供需矛盾,如何在大保护的前提下有序开发、合理利用,将“绿水青山”科学、合理地转化为“金山银山”是现阶段亟需面对和解决的重大问题。本文以神农架林区为例,基于多源地理空间数据,运用XGBoost算法对其生态旅游适宜性进行评估,得出如下结论:(1)基于XGBoost算法的生态旅游适宜性评价模型融合了机器学习技术与数据挖掘思想并取得了良好的分类效果,模型在10折交叉验证下的整体分类精度为89.44%,同时兼有良好的召回率(89.68%),F1分数为0.874 5,兼顾了精确度和召回率,模型AUC值为0.959 3,模型整体分类性能表现优秀;(2)根据特征重要性排序结果,生态环境要素NDVI(26.86%)、年平均气温(11.61%)和社会经济因素距道路距离(8.90%)对模型贡献度最高,位列所有特征前3位;(3)生态旅游适宜性分类结果表明,神农架林区整体生态旅游资源丰富,高度适宜区、中度适宜区、边际适宜区和不适宜区的覆盖面积分别占林区总面积的44.13%、15.93%、11.89%和28.05%。本文研究方法突破了传统生态旅游适宜性评估方法主观性较强的局限,立足于数据挖掘思想和机器学习技术解决实际问题,可为区域国土空间规划、乡村振兴战略和生态旅游资源整合提供决策依据。

In recent years,with the improvement of public awareness of environmental protection,the pursuit of harmonious coexistence between man and nature of ecotourism has attracted more and more people's attention.In the face of rich and diverse ecotourism resources and the contradiction between supply and demand of domestic ecotourism,how to orderly develop and make rational use of resources under the premise of maximum protection,and transform "lucid waters and lush mountains" into "gold and silver mountains" scientifically and rationally is a major issue to be addressed at present.Taking Shennongjia Forestry District as an example,based on multi-source geospatial data,we use the XGBoost algorithm to evaluate its ecotourism suitability.The following conclusions are drawn:(1) The ecotourism suitability evaluation model based on XGBoost algorithm integrates machine learning technology and achieves good classification results with the idea of data mining.The overall classification accuracy of the model under 10-fold cross-validation is 89.44%,with a high recall rate(89.68%).The F1 score is 0.8745,based on both the precision and recall.The AUC value of the model is 0.9593,and the overall classification performance of the model is excellent;(2) According to the ranking results of feature importance,the NDVI of ecological environment elements(26.86%),annual average temperature(11.61%),and distance from social and economic factors to the road(8.90%) has the highest contribution to the model;(3) The classification results of ecotourism suitability show that the overall ecotourism resources in Shennongjia Forestry District are rich.Highly suitable areas,moderately suitable areas,marginally suitable areas,and unsuitable areas account for 44.13%,15.93%,11.89%,and 28.05% of the total forest area,respectively.The research method of this paper breaks through the limitations of traditional ecotourism suitability assessment methods which are highly subjective,and solves practical problems based on data mining ideas and machine learning technology.