Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azarbaijan, Iran.
GTEV-ReX Limited, Swindon, UK.
Sci Total Environ. 2017 Jan 1;574:691-706. doi: 10.1016/j.scitotenv.2016.09.093. Epub 2016 Oct 14.
This research presents a Supervised Intelligent Committee Machine (SICM) model to assess groundwater vulnerability indices of an aquifer. SICM uses Artificial Neural Networks (ANN) to overarch three Artificial Intelligence (AI) models: Support Vector Machine (SVM), Neuro-Fuzzy (NF) and Gene Expression Programming (GEP). Each model uses the DRASTIC index, the acronym of 7 geological, hydrological and hydrogeological parameters, which collectively represents intrinsic (or natural) vulnerability and gives a sense of contaminants, such as nitrate-N, penetrating aquifers from the surface. These models are trained to modify or condition their DRASTIC index values by measured nitrate-N concentration. The three AI-techniques often perform similarly but have differences as well and therefore SICM exploits the situation to improve the modeled values by producing a hybrid modeling results through selecting better performing SVM, NF and GEP components. The models of the study area at Ardabil aquifer show that the vulnerability indices by the DRASTIC framework produces sharp fronts but AI models smoothen the fronts and reflect a better correlation with observed nitrate values; SICM improves on the performances of three AI models and cope well with heterogeneity and uncertain parameters.
本研究提出了一种有监督的智能委员会机（SICM）模型，用于评估含水层的地下水脆弱性指数。SICM 使用人工神经网络（ANN）来涵盖三种人工智能（AI）模型：支持向量机（SVM）、神经模糊（NF）和基因表达编程（GEP）。每个模型都使用 DRASTIC 指数，这是 7 个地质、水文和水文地质参数的缩写，它共同代表内在（或自然）脆弱性，并对污染物（如硝酸盐-N）从地表穿透含水层有一定的感知。这些模型经过训练，可以通过测量的硝酸盐-N 浓度来修改或调整其 DRASTIC 指数值。这三种 AI 技术的性能通常相似，但也存在差异，因此 SICM 通过选择性能更好的 SVM、NF 和 GEP 组件来产生混合建模结果，从而利用这种情况来提高模型值。研究区域在阿尔达比勒含水层的模型表明，DRASTIC 框架产生的脆弱性指数会产生明显的前沿，但 AI 模型会使这些前沿变得平滑，并与观测到的硝酸盐值更好地相关；SICM 提高了三种 AI 模型的性能，并且能够很好地处理异质性和不确定参数。
