[论文解读] Groundwater vulnerability assessment in semi-arid regions using GIS-based DRASTIC models and FUZZY AHP: South Chott Hodna

Lakhdar Seraiche, Mostafa Dougha+4 more|arXiv (Cornell University)|Jan 16, 2026

Groundwater and Isotope Geochemistry被引用 0

一句话总结

本文提出一个基于 GIS 的框架，通过土地利用数据和 AHP 与模糊 AHP 的权重化来增强 DRASTIC 的地下水脆弱性模型，并以 70 个井的硝酸盐数据进行验证，AUC 最高达到 0.951。

ABSTRACT

Groundwater vulnerability is a major concern in arid regions worldwide, where population growth and intensive agriculture increase the risks of depletion and contamination. This study proposes a hybrid groundwater vulnerability assessment framework that improves the conventional DRASTIC model by integrating land-use data and applying advanced weighting techniques, namely the Analytical Hierarchy Process (AHP) and its fuzzy logic variant (Fuzzy AHP). This method makes expert-based weighting less subjective, better captures anthropogenic effects, and facilitates adaptation to challenging situations and limited data. Four vulnerability maps were produced using Geographic Information Systems (GIS): DRASTIC, DRASTIC_LU, AHP DRASTIC_LU, and Fuzzy AHP DRASTIC_LU. We used nitrate levels from 70 wells to verify our work. We found that agricultural areas, especially those above the alluvial aquifer, were the most vulnerable. The ROC curve analysis showed that the model improved over time, with the area under the curve (AUC) values of 0.812 for DRASTIC, 0.864 for DRASTIC_LU, 0.875 for AHP DRASTIC_LU, and 0.951 for fuzzy AHP DRASTIC_LU. These results show that fuzzy AHP DRASTIC_LU makes groundwater risk assessments much more. The GIS-based hybrid models offer a scalable and transferable method for mapping vulnerability, but they also provide local and regional water resource managers with useful information.

研究动机与目标

• 在人口增长和农业活动增加的半干旱地区推动地下水脆弱性评估。
• 开发一个将土地利用数据整合到 DRASTIC 的混合框架，并应用 AHP 和模糊 AHP 进行权重分配。
• 生成并比较四张脆弱性地图：DRASTIC、DRASTIC_LU、AHP DRASTIC_LU，以及 Fuzzy AHP DRASTIC_LU。
• 使用 70 口井的硝酸盐浓度作为验证数据来验证模型性能。

提出的方法

• 使用基于 GIS 的处理构建四张脆弱性地图：DRASTIC、DRASTIC_LU、AHP DRASTIC_LU 和 Fuzzy AHP DRASTIC_LU。
• 将土地利用数据并入 DRASTIC 框架（DRASTIC_LU）。
• 应用层次分析法（AHP）为 DRASTIC_LU 模型导出专家权重。
• 应用模糊 AHP 为 DRASTIC_LU 模型导出模糊专家权重。
• 以 70 口井的硝酸盐数据作为验证数据来评估模型。
• 通过 ROC/AUC 指标评估性能（0.812、0.864、0.875、0.951）。

实验结果

研究问题

• RQ1 将土地利用数据纳入是否会影响半干旱区域基于 DRASTIC 的地下水脆弱性评估？
• RQ2 相对于标准 DRASTIC 方法，AHP 与模糊 AHP 权重是否提高基于 GIS 的脆弱性模型的预测性能？
• RQ3 哪一张脆弱性地图（DRASTIC、DRASTIC_LU、AHP DRASTIC_LU、Fuzzy AHP DRASTIC_LU）最能解释井位观测的硝酸盐分布？
• RQ4 从 DRASTIC 提升到 Fuzzy AHP DRASTIC_LU 时，模型性能的相对提升是多少？

主要发现

• 农用区，尤其在冲积含水层之上，显示出最高的脆弱性。
• DRASTIC_AUC = 0.812；DRASTIC_LU_AUC = 0.864；AHP DRASTIC_LU_AUC = 0.875；FUZZY AHP DRASTIC_LU_AUC = 0.951。
• 混合 GIS 基于的模型具有可扩展性，适用于本地与区域水资源管理。
• 该方法通过权重化方法更好地捕捉人为影响并减少专家主观性。