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利用袋装集成优化 DRASTIC 框架对受污染风险的地下水区域进行划分。

Delimitation of groundwater zones under contamination risk using a bagged ensemble of optimized DRASTIC frameworks.

机构信息

Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, Iran.

Department of Bioresource Engineering, McGill University, 21111 Lakeshore, Ste Anne de Bellevue, Quebec, H9X3V9, Canada.

出版信息

Environ Sci Pollut Res Int. 2019 Mar;26(8):8325-8339. doi: 10.1007/s11356-019-04252-9. Epub 2019 Jan 31.

Abstract

Developing a reliable groundwater vulnerability and contamination risk map is very important for groundwater management and protection. This study aims to compare various modified DRASTIC vulnerability frameworks based on rate calibration using the Wilcoxon rank-sum test (WRST), frequency ratio (FR) and weight optimization using the correlation coefficient (CC), the analytic hierarchy process (AHP), and genetic algorithms (GA), as well as to introduce, for the first time, an aggregated approach based on a bagging ensemble to develop a combined modified DRASTIC model. This research was conducted in the Khoy plain, NW Iran. To develop a typical DRASTIC map, seven DRASTIC data layers were generated, weighted, and then overlaid in ArcGIS. The nitrate (NO) concentrations at 54 sites in the study area were used to validate the models by calculating the correlation coefficient (r) between the vulnerability/risk indices and NO concentrations. The calculated r value for the typical DRASTIC was 0.12. A sensitivity analysis reveals that the impact of the vadose zone and conductivity parameters with mean variation indices of 22.2 and 7.5%, respectively, have the highest and lowest influence on aquifer vulnerability. The r values increased for all the optimized frameworks. The results show that the WRST and GA methods are the most effective methods for calibration and optimization of DRASTIC rates and weights, with the WRST-GA-DRASTIC model obtaining an r value of 0.64. A bagging ensemble model was employed to combine the advantages of each standalone model. The bagging ensemble model yields an r value of 0.67. The ensemble model has the potential to increase the r value further than both the standalone optimized frameworks and the typical DRASTIC approach. In terms of spatial distribution class area (%), the bagging ensemble-DRASTIC model demonstrates that the moderate and low contamination risk classes with 16.4 and 23.1% of the total area cover the lowest and highest parts of the plain.

摘要

开发可靠的地下水脆弱性和污染风险图对于地下水管理和保护非常重要。本研究旨在比较各种基于比率校准的改进 DRASTIC 脆弱性框架,使用 Wilcoxon 秩和检验 (WRST)、频率比 (FR) 和基于相关系数 (CC)、层次分析法 (AHP) 和遗传算法 (GA) 的权重优化,以及首次引入基于装袋集成的综合方法来开发综合改进 DRASTIC 模型。本研究在伊朗西北部霍伊平原进行。为了开发典型的 DRASTIC 图,生成了七个 DRASTIC 数据层,在 ArcGIS 中进行加权,然后叠加。使用研究区域 54 个地点的硝酸盐 (NO) 浓度通过计算脆弱性/风险指数与 NO 浓度之间的相关系数 (r) 来验证模型。典型 DRASTIC 的计算 r 值为 0.12。敏感性分析表明,包气带和电导率参数的影响最大,其平均变化指数分别为 22.2%和 7.5%,对含水层脆弱性的影响最小。所有优化框架的 r 值均增加。结果表明,WRST 和 GA 方法是校准和优化 DRASTIC 比率和权重的最有效方法,WRST-GA-DRASTIC 模型的 r 值为 0.64。采用袋装集成模型来结合每个独立模型的优势。袋装集成模型的 r 值为 0.67。与独立优化框架和典型 DRASTIC 方法相比,集成模型具有进一步提高 r 值的潜力。在空间分布类别面积(%)方面,袋装集成-DRASTIC 模型表明,具有 16.4%和 23.1%总区域覆盖的低中和低污染风险类别涵盖了平原的最低和最高部分。

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