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Watershed Planning within a Quantitative Scenario Analysis Framework
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预测地下水质量指数以评估适合饮用目的,使用平均神经网络和地理空间分析
Seok Hyun Ahn1, Do Hwan Jeong2, MoonSu Kim2
1Department of Environmental Engineering, Yonsei University, Wonju 26493, South Korea.
Ecotoxicology and environmental safety
|September 20, 2023
概括
超过50%的韩国水井对饮用水不安全,需要改变政策. 机器学习和空间分析为更好的管理提供了准确的地下水质量评估和脆弱性映射.
科学领域:
- 环境科学 环境科学
- 水文地质学 水文地质学
- 数据科学数据科学数据科学
背景情况:
- 传统的地下水质量指数在参数选择,地理范围和可扩展性方面存在局限性.
- 有效的地下水质量管理对于公众健康和生态稳定至关重要.
研究的目的:
- 使用机器学习和空间分析开发和验证先进的地下水质量指数 (GQI).
- 评估韩国的可饮地下水质量,并确定脆弱区域.
- 为改善地下水资源管理提供政策修订信息.
主要方法:
- 利用了来自韩国3552个井的28个水质参数的综合数据集.
- 应用机器学习,特别是平均神经网络模型,用于GQI等级预测.
- 集成的2D空间分析与机器学习,以提高不均分布的数据的预测准确性.
主要成果:
- 超过50%的8326个评估的井被发现不适合饮用水.
- 平均神经网络模型在预测GQI等级方面达到约95%的准确性.
- 机器学习和空间分析的结合提高了预测准确度,并使地下水脆弱性的直观可视化成为可能.
结论:
- 该研究强调,韩国对地下水质量政策的审查至关重要.
- 综合机器学习和空间分析方法为未来的地下水质量管理和脆弱性评估提供了坚实的框架.
- 这种跨学科的方法为适应性地下水管理策略提供了可扩展和准确的解决方案.
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