To prioritize archaeological site inventory and protection at Glen Canyon National Recreation Area (Glen Canyon NRA), predictive probability models were developed using the Random Forests machine learning method. Glen Canyon NRA consists of approximately 5,000 km2 in Arizona and Utah containing evidence of multiple prehistoric cultures spanning at least 10,000 years. Large portions of Glen Canyon NRA have never been inventoried for archaeological resources. Archaeological sites are potentially subject to irreparable damage along roads and receding Lake Powell shorelines accessible to off-road vehicles and boats. The diverse cultural history, highly variable physiographic environments, archaeological site data inconsistencies, and limited available model variable data provided challenges. Model results improved by classifying sites, based on their inferred usage, and distinguishing distinct physiographic regions within the larger NRA landscape. Model results are being used to target ongoing field surveys. Initial validation is positive, with new sites discovered in areas of predicted high probability.

中文翻译：

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在美国格伦峡谷国家游乐区中，使用预测概率模型对考古调查清单和保护进行优先排序

为了优先考虑格伦峡谷国家游乐区（格伦峡谷NRA）的考古现场存货和保护，使用随机森林机器学习方法开发了预测概率模型。格伦峡谷NRA在亚利桑那州和犹他州约有5,000平方公里，其中包含至少10,000年的多种史前文化的证据。从来没有对格伦峡谷NRA的大部分考古资源进行盘点。考古遗址可能会受到公路和后退的鲍威尔湖沿岸道路的不可弥补的破坏，越野车辆和船只可以到达。多样的文化历史，高度变化的地貌环境，考古现场数据的不一致性以及有限的可用模型变量数据带来了挑战。通过对站点进行分类来改善模型结果，根据它们的推断用法，并在较大的NRA风景中区分明显的地貌区域。模型结果用于确定正在进行的实地调查。初步验证是肯定的，在预测的高概率区域中发现了新站点。