概括
机器学习模型被用来预测印度五个州的地下水化物污染. 随机森林 (RF) 模型展示了最高的准确性,提供了一种可靠的方法来识别高风险区域.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 地下水的化物污染是印度的一个重大问题,造成骨和牙化等健康风险.
- 长期暴露于高化物水平需要在受影响地区进行详细的分析和预测.
研究的目的:
- 利用机器学习分析和预测印度五个主要受影响州地下水中的化物度.
- 确定用于化物污染预测的最有效的机器学习算法.
主要方法:
- 采用了各种机器学习算法,包括K-近邻 (KNN),逻辑回归 (LR),随机森林 (RF),支持矢量分类器 (SVC) 等.
- 利用相关性矩阵来选择预测变量,并使用准确性,精度,回忆,错误率和接收器操作曲线来评估模型性能.
- 由于数据集偏差,在数据重新采样之前和之后评估模型性能.
主要成果:
- 随机森林 (RF) 模型被确定为预测地下水中的化物污染的优越算法.
- 性能指标表明RF在处理偏斜数据集和准确预测化物水平方面的稳定性.
结论:
- 机器学习,特别是射频模型,为预测印度各州地下水化物污染提供了有效的工具.
- 这种预测能力可以帮助制定有针对性的干预措施和公共卫生战略,以减轻化风险.
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