土地沉降易感性映射:一种新的方法来提高决策茎分类 (DSC) 的性能,并将其与四种机器学习算法结合起来
Rui Zhao1,2, Alireza Arabameri3, M Santosh4,5
1School of Energy and Power Engineering, Xihua University, Chengdu, 610039, China.
Environmental science and pollution research international
|February 1, 2024
概括
土地沉降是一个全球威胁,由地下水枯竭加剧,需要准确地绘制地图以缓解. 一种新的混合方法将决策茎分类与交替决策树 (DSC-ADTree) 结合起来,显著提高了土地沉降易感性测绘的准确性.
科学领域:
- 地质科学 地质科学
- 环境科学 环境科学
- 计算机科学 计算机科学
背景情况:
- 土地沉降是一个重要的全球环境危险,特别是在干旱和半干旱地区,主要是由地下水枯竭驱动的.
- 这种现象导致严重的环境退化和社会经济挑战,需要准确的预测工具.
- 土地沉降易感地图 (LSSM) 对于管理脆弱地区和减轻沉降影响至关重要.
研究的目的:
- 开发和评估新的机器学习方法,以提高土地沉降易感性测绘 (LSSSM).
- 提高LSSMs的准确性和可靠性,以改善风险管理和预防策略.
- 将新混合算法的性能与LSSSM中的现有机器学习模型进行比较.
主要方法:
- 通过将决策干分类 (DSC) 与各种机器学习算法 (MLA) 集成,开发了一种新的方法.
- 混合模型包括DSC与天真贝叶斯树 (NBTree),J48决策树,交替决策树 (ADTree),物流模型树 (LMT) 和支持矢量机器 (SVM) 结合.
- 使用ROC-AUC,精度,RMSE和F-score等指标严格评估模型性能,94个沉降位置中有70%用于培训,30%用于验证.
主要成果:
- 新的DSC-ADTree混合算法在LSSSM中表现出卓越的性能,准确度最高 (AUC = 0.983).
- 其他混合型号也表现出强的表现:DSC-J48 (AUC = 0.976),DSC-NBTree (AUC = 0.959),DSC-LMT (AUC = 0.948) 和DSC-SVM (AUC = 0.939). 其他混合型号也表现出强的表现:DSC-J48 (AUC = 0.976),DSC-NBTree (AUC = 0.959),DSC-LMT (AUC = 0.948) 和DSC-SVM (AUC = 0.939).
- 独立的DSC模型实现了0.911的AUC,突出显示了混合方法的好处.
结论:
- 开发的DSC-ADTree混合算法提供了一个高度准确和可靠的方法,用于土地沉降易感测绘.
- 生成的LSSSM为当局提供了有效的工具来管理沉降风险并实施预防措施.
- 这项研究强调了先进的机器学习技术在解决土地沉降等关键环境挑战方面的潜力.
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