相关实验视频
Updated: Jun 18, 2025

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Watershed Planning within a Quantitative Scenario Analysis Framework
Published on: July 24, 2016
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由物理机制指导的可解释机器学习揭示了在动态土地使用变化下下水的驱动因素
Shuli Wang1, Yitian Liu1, Wei Wang1
1School of Water and Environment, Chang'an University, Xi'an, 710061, China; Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, Chang'an University, Xi'an, 710061, China.
Journal of environmental management
|July 27, 2024
概括
人类活动对流域流水产生影响. 这项研究使用可解释的机器学习来揭示土地利用变化,如农业和森林,如何动态影响水流量,确定关键的气象值.
科学领域:
- 水文和水资源管理 水文和水资源管理
- 环境科学 环境科学
- 机器学习应用 机器学习应用
背景情况:
- 人类活动显著改变了流域水平衡和流水模式.
- 准确地确定对动态土地利用变化的流水反应至关重要.
- 传统的机器学习模型往往缺乏可解释性,阻碍了驾驶员的识别.
研究的目的:
- 采用可解释的机器学习方法,以反向推断流水的动态决定因素.
- 分析土地使用变化对黄河中部下水的影响.
- 揭示土地利用,气象变量和排水产生之间的相互作用机制.
主要方法:
- 在四个时期内分析了黄河宁夏部分的土地使用变化.
- 利用土壤和水资源评估工具 (SWAT) 来生成水文和气象数据.
- 使用极端梯度提升 (XGBoost) 来进行流水模拟,并使用夏普利添加式扩展 (SHAP) 来进行解释.
主要成果:
- 在研究区域观察到越来越频繁的土地使用类型交换.
- XGBoost有效地模拟了下水,SHAP显示了动态的土地使用影响.
- 农业用地 (AGRL) 促进排水效应减弱,而森林 (FRST) 加强了它们的抑制效应.
- 确定了相对湿度 (RH),最大温度 (MaxT) 和最低温度 (MinT) 的值影响.
结论:
- 可解释机器学习 (XGBoost-SHAP) 成功识别了受土地使用影响的动态流失驱动因素.
- 土地利用政策应该考虑农业的削弱效应和森林对冲流的增强效应.
- 了解气象变量的值效应是管理在不断变化的条件下流失的关键.
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