Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
GTEV-ReX Limited, Swindon, UK.
Environ Sci Pollut Res Int. 2017 Mar;24(9):8562-8577. doi: 10.1007/s11356-017-8489-4. Epub 2017 Feb 13.
Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.
不同模糊逻辑 (FL) 模型评估的含水层脆弱性指数往往会产生不同的值，没有理论或经验依据来建立有效的基准线，也无法在建模结果和基准线之间建立比较基础（如果有的话）。因此，本研究提出了一种有监督委员会模糊逻辑 (SCFL) 方法，该方法使用人工神经网络来涵盖和组合一系列 FL 模型。这些指数采用广泛使用的 DRASTIC 框架表示，其中包括地质、水文和水文地质参数，这些参数通常存在不确定性。DRASTIC 指数共同表示内在（或自然）脆弱性，并能感知污染物，如硝酸盐-N，从地表渗透到含水层。研究区域是伊朗西北部阿尔达比勒省阿尔达比勒平原的一个含水层。FL 技术可提高脆弱性指数，包括 Sugeno 模糊逻辑 (SFL)、Mamdani 模糊逻辑 (MFL) 和 Larsen 模糊逻辑 (LFL)。由于估计的 DRASTIC 脆弱性指数值与硝酸盐-N 值之间的相关性低至 0.4，因此 FL 模型（SFL、MFL 和 LFL）显著提高了其相关性，其表现方式相似但存在差异。SCFL 利用它们的协同作用，并利用硝酸盐-N 值“调节”的 FL 建模结果将相关性提高到 0.9 以上。
