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使用基于GIS的机器学习方法对酸盐地下水污染的风险评估:在中国安平原北部的案例研究
Kai Chen1, Qimeng Liu2, Tingting Yang2
1School of Earth and Environment, Anhui University of Science & Technology, Huainan 232001, China; State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science & Technology, Huainan 232001, China.
Journal of contaminant hydrology
|January 19, 2024
概括
长期的农业威胁到安北部平原的地下水. 这项研究使用LightGBM和随机森林模型绘制地下水酸盐污染风险图,确定南部高风险地区.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 地理空间分析是什么
背景情况:
- 北安平原的长期农业做法引发了人们对可持续地下水开发的担忧.
- 识别具有高地下水污染风险的地区对于有效保护区域水资源至关重要.
研究的目的:
- 评估北安平原地下水酸盐 (NO3-) 污染风险.
- 使用先进的机器学习算法开发准确的地下水污染概率图.
主要方法:
- 收集了60个地下水样本来分析酸盐度.
- 利用了10个环境和地质因素 (海拔,土地覆盖,降雨等). 作为输入层.
- 在GIS框架内使用LightGBM和随机森林算法进行空间预测.
主要成果:
- 地下水中酸盐度在4.3至73.6毫克/升之间,其中大多数超过18.3毫克/升的值.
- 光GBM和射频模型都预测了南部平原的高酸盐污染风险.
- 与射频相比,LightGBM表现出更高的性能 (卡帕=0.84),比RF.
结论:
- 降水 (38.14%) 和海拔 (21.4%) 被确定为地下水酸盐污染风险的主要因素.
- 该研究强调需要进一步调查人类因素,以加强污染预防战略.
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