[基于机器学习方法的长江流域农田流失负荷估计]
Yu-Fu Zhang1,2, Zhe-Qi Pan1, Ding-Jiang Chen1,2,3
1College of Environmental & Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Huan jing ke xue= Huanjing kexue
|July 12, 2023
概括
了解耕地 (N) 流失是控制污染的关键. 这项研究使用机器学习来识别N损失因子,并预测长江流域的负载,提供了一种新的建模方法.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 数据科学数据科学数据科学
背景情况:
- 对有效的污染控制策略来说,量化耕地 (N) 流失至关重要.
- 来自农田的非点源污染严重影响水质.
研究的目的:
- 确定影响长江流域 (YRB) 高地和田总气 (TN) 流失的关键因素.
- 开发和验证基于机器学习的模型,用于预测耕地TN流失负载.
- 估计YRB中TN流失负载的总量,并在不同情景下预测潜在的减少.
主要方法:
- 采用相关性分析,结构方程建模,差异分解和机器学习算法 (包括随机森林).
- 开发了一个使用随机森林算法的预测模型,用于TN流失率.
- 使用2013年为YRB开发的模型量化TN流失负载.
主要成果:
- 排水深度,土壤N含量和肥料率是高地TN损失的主要驱动因素;对田的排水深度和肥料率.
- 随机森林模型在预测TN流失率方面取得了很高的准确性 (R2=0.65-0.94).
- 据估计,YRB的农田TN总损失负荷为0.47 Tg·a−1,其中58%来自中游和下游地区.
结论:
- 综合水,肥料和土壤营养管理对于减轻YRB的污染至关重要.
- 中游和下游地区需要优先管理N流量控制的管理策略.
- 开发的机器学习方法提供了一种可靠的方法来估计区域和流域耕地TN损失.
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