受到气候变化和人口增长影响的沿海水层的海营养使用机器学习方法
N L Kushwaha1, Kallem Sushanth2, Abhishek Patel3
1Division of Agricultural Engineering, ICAR-Indian Agricultural Research Institute, New Delhi, 110012, India.
Journal of environmental management
|September 27, 2024
概括
海营养有效地管理了沿海地下水的盐水入侵 (SWI). 随机森林 (RF) 模型在预测SWI方面表现出卓越的准确性,为水资源管理提供了有价值的工具.
科学领域:
- 水文地质学 水文地质学
- 沿海工程 沿海工程
- 机器学习 机器学习
背景情况:
- 沿海地下水资源容易受到盐水入侵的影响 (SWI).
- 海营养是一种在沿海水层中减轻SWI的策略.
- 未来的挑战包括海平面上升和增加淡水提取.
研究的目的:
- 评估机器学习 (ML) 模型,以预测海养殖场景下的SWI.
- 为了比较7个ML模型在佛罗里达州比斯凯内水层的性能.
- 用有限的数据确定最有效的SWI预测ML模型.
主要方法:
- 一个3D海水模型被用来模拟SWI与海营养.
- 开发了七种ML模型并进行了比较评估:AR,SVM,REPTree,Bagging,RSS,RF和ANN.
- 使用统计指标 (R2,NSE,MAE,RMSE,RRSE) 和图形分析来评估模型的性能.
主要成果:
- SVM,包装,RSS和射频模型显示出预测SWI的巨大潜力.
- 随机森林 (RF) 模型在测试过程中实现了最高的准确性,R2为0.999和NSE为0.999.
- 射频显示出与观察到的SWI值有很好的一致性.
结论:
- 随机森林 (RF) 模型在预测沿海地区的盐水入侵 (SWI) 方面非常有效.
- 射频为准确的SWI预测和可持续的水资源管理提供了有价值的工具.
- 机器学习方法,特别是射频,对于使用有限的数据来管理SWI非常有希望.
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