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从碎片到碎片:从政策到实践,通过人工智能驱动的多尺度空间规划来实现地下水酸盐管理

Amir Naghibi¹, Kourosh Ahmadi², Seyed Mohsen Mousavi³

¹Division of Water Resources Engineering, Lund University, Lund, Sweden; Centre for Advanced Middle Eastern Studies, Lund University, Lund, Sweden.

|February 26, 2026

概括

扩散的酸盐污染威胁到饮用水. 将土地覆盖的组成和碎片化纳入预测模型可以显著改善11%的酸盐脆弱性映射.

科学领域:

环境科学环境科学
水文学的水文学
地理空间分析是什么

背景情况:

分散的酸盐污染对饮用水质量构成重大威胁.
现有的预测模型往往忽视了土地覆盖构成和景观碎片化对酸盐漏的综合影响.
了解这些空间因素对于有效的水资源管理至关重要.

研究的目的:

开发和验证一个新的多尺度框架来预测酸盐脆弱性.
评估将土地覆盖率和景观碎片化指标整合到预测模型中的影响.
确定与政策相关的减轻酸盐污染的战略.

主要方法:

一个多尺度的框架,将水气候,土壤和人为数据与土地覆盖率和碎片化指标 (50-500米网格) 整合在一起.
基于人工智能 (AI) 的模型使用特征选择从116个初始因素中确定前30个预测因素.
比较建模: (i) 没有土地使用数据, (ii) 有土地使用组成和碎片化数据.
使用多层感知子编码器 (MLP-encoder) 进行顺序分类,并与比例概率顺序回归进行基准测试.

主要成果:

纳入土地覆盖率和景观碎片化的模型显示,准确度提高了11%.
特征选择确定了关键预测因素,突出了空间构成和配置的重要性.
改进后的模型提供了更准确,空间更明确的酸盐脆弱性地图.

结论:

在中间尺度上整合土地利用成分和碎片化显著提高了酸盐脆弱性预测.
这些发现为设计采水区管理和地下水保护干预措施提供了空间明确,可操作的见解.
森林湿地综合体的保护和限制大型耕地块是政策制定者推的战略.

关键词:

决策支持系统决策支持系统丹麦丹麦丹麦丹麦丹麦在GeoAIAI上,你会发现.治理治理治理治理治理土地管理土地管理政策设计政策设计水的质量水的质量零污染零污染没有污染

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