Department of Watershed Management Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of GIS/RS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Iran.
Sci Total Environ. 2021 May 1;767:145416. doi: 10.1016/j.scitotenv.2021.145416. Epub 2021 Jan 27.
Due to excessive exploitation, groundwater resources of coastal regions are exposed to seawater intrusion. Therefore, vulnerability assessments are essential for the quantitative and qualitative management of these resources. The GALDIT model is the most widely used approach for coastal aquifer vulnerability assessment, but suffers from subjectivity of the identification of rates and weights. This study aimes at developing a new hybrid framework for improving the accuracy of coastal aquifer vulnerability assessment using various statistical, metaheuristic, and Multi-Attribute Decision Making (MADM) methods to improve the GALDIT model. The Gharesoo-Gorgan Rood coastal aquifer in northern Iran is used as study site. In order to meet this aim, the Differential Evolution (DE) and Biogeography-Based Optimization (BBO) metaheuristic algorithms were employed to optimize the GALDIT weights. In addition, a novel MADM method, named Step-wise Weight Assessment Ratio Analysis (SWARA), and the bivariate statistical method called statistical index (SI) were used to modify the GALDIT ratings. Finally, correlation coefficients between the maps obtained from each method and Total Dissolved Solid (TDS) as an indicator of seawater intrusion were computed to evaluate the models' prediction power. Correlation coefficients of 0.72, 0.75, 0.76 and 0.78 were obtained for the GALDIT, GALDIT, GALDIT and GALDIT models, respectively. The results from the GALDIT model outperformed all other models at improving the accuracy of the vulnerability assessment. Moreover, the statistical-metaheuristic method yielded more accurate results than SWARA-metaheuristic hybrid models. The vulnerability map of the studied region indicates that the northwestern and western areas are very highly vulnerable. According to GALDIT model, 42%, 17%, 18% and 22% of the aquifer areas respectively have a low, medium, high and very high vulnerability to seawater intrusion. The research findings could be applied by regional authorities to manage and protect groundwater resources.
由于过度开采，沿海地区的地下水资源面临海水入侵。因此，进行脆弱性评估对于这些资源的定量和定性管理至关重要。GALDIT 模型是最广泛用于沿海含水层脆弱性评估的方法，但存在识别速率和权重的主观性问题。本研究旨在开发一种新的混合框架，以提高使用各种统计、元启发式和多属性决策 (MADM) 方法改进 GALDIT 模型的沿海含水层脆弱性评估的准确性。伊朗北部的 Gharesoo-Gorgan Rood 沿海含水层被用作研究地点。为了达到这个目的，使用了差分进化 (DE) 和生物地理学优化 (BBO) 元启发式算法来优化 GALDIT 权重。此外，还使用了一种新的 MADM 方法，名为逐步权重评估比率分析 (SWARA)，以及二元统计方法称为统计指数 (SI)，用于修改 GALDIT 评级。最后，计算了每种方法获得的地图与作为海水入侵指标的总溶解固体 (TDS) 之间的相关系数，以评估模型的预测能力。对于 GALDIT、GALDIT、GALDIT 和 GALDIT 模型，获得的相关系数分别为 0.72、0.75、0.76 和 0.78。GALDIT 模型在提高脆弱性评估的准确性方面的结果优于所有其他模型。此外，统计-元启发式方法比 SWARA-元启发式混合模型产生更准确的结果。研究区域的脆弱性图表明，西北部和西部地区非常脆弱。根据 GALDIT 模型，分别有 42%、17%、18%和 22%的含水层区域对海水入侵具有低、中、高和非常高的脆弱性。研究结果可由地区当局应用于地下水管理和保护。
