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[基于综合多源空间时间数据的地下水硫酸盐预测模型]

Ru-Yue Li^1,2,3, Yan-Yan Zeng^1,2,3, Jin-Long Zhou^1,2,3

¹College of Water Conservancy and Civil Engineering, Xinjiang Agricultural University, Urumqi 830052, China.

Huan jing ke xue= Huanjing kexue

|June 19, 2024

概括

准确地预测地下水硫酸盐 (SO42-) 对水质管理至关重要. 一个贝叶斯优化算法优化的随机森林回归模型确定了pH,海拔和赤裸的土地作为关键因素,提高了空间分布的准确性.

科学领域:

水地质学和环境科学:专注于地下水化学和空间变异性分析.

背景情况:

地下水质量评估和管理需要对污染物度进行准确的空间预测.
硫酸盐 (SO42-) 是一个关键的地下水污染物,影响水质和可用性.
了解SO42-的空间变化对于有效的区域地下水管理至关重要.

研究的目的:

开发和验证一个高度准确的模型来预测地下水SO42-度的空间分布.
确定影响地下水中SO42-度的关键环境因素.
为了生成SO42-分布在Yarkant河流域的预测地图.

主要方法:

利用了多个来源的时空数据,包括土地覆盖面,土壤参数,数字海拔和地下水pH值.
采用贝叶斯优化算法 (BOA) 来优化随机森林回归 (RFR) 模型.
综合地理统计插值作为空间分布建模的辅助工具.

主要成果:

确定了地下水的pH值,地面高度 (GE) 和裸露土地的百分比 (BAR) 作为最有影响力的因素,与SO42-度负相关.
在纳入辅助样本后,BOA-RFR模型实现了超过0.96的预测准确度 (R2).
观察到预测准确度的显著改善,根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 的减少.

结论:

优化的BOA-RFR模型有效地预测了地下水中的空间SO42-趋势.
像pH值,海拔和赤裸的土地这样的关键因素显著控制地下水SO42-水平.
在Yarkant河流域的东北部确定了高SO42-地下水区,这表明一个日益扩大的关注区域.

关键词:

贝叶斯优化算法 (BOA)地下水硫酸盐的存在多种来源的时空数据.随机森林回归 (RFR)空间分布预测的预测

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