使用机器学习方法预测斯洛伐克有防御功能的历史物体的潜在出现情况
Jana Vojteková1, Saeid Janizadeh2, Matej Vojtek3,4
1Department of Geography, Geoinformatics and Regional Development, Faculty of Natural Sciences and Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia.
Scientific reports
|December 5, 2024
概括
这项研究使用机器学习预测了斯洛伐克的历史防御物体位置. 随机森林模型获得了最高的准确性,有助于未来的考古发现.
科学领域:
- 考古学的考古学
- 地理空间分析的研究.
- 机器学习 机器学习
背景情况:
- 具有防御功能 (HODF) 的历史物体对于了解过去的人类活动和定居模式至关重要.
- 斯洛伐克的许多HODF仍然未被发现,限制了全面的历史和考古研究.
- 预测建模为定位未经记录的历史遗址提供了一种新的方法.
研究的目的:
- 预测斯洛伐克有防御功能的未经记录的历史物体 (HODF) 的潜在位置.
- 评估三种机器学习算法的有效性,用于考古遗址预测.
- 确定影响HODF发生的关键环境因素.
主要方法:
- 利用支持向量机器,k-最近邻居和随机森林算法进行预测建模.
- 使用605个已记录的HODF数据集训练模型,考虑海拔,靠近河流和定居点,岩石学和地质生态系统.
- 使用70/30培训/测试分割和AUC-ROC值验证模型性能,并对新发现的HODF进行额外验证.
主要成果:
- 这三种机器学习模型在测试数据集上都实现了AUC-ROC值超过0.74.
- 随机森林模型显示出最高的预测性能,AUC-ROC为0.79.
- 模型的预测得到了考古学研究中最近发现的HODF的证实.
结论:
- 机器学习,特别是随机森林算法,对于预测历史防御物体的位置是有效的.
- 确定的环境因素是HODF分布的重要预测因素.
- 这种预测方法可以指导未来的考古调查,并加强斯洛伐克历史遗产的发现.
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