Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran.
Department of Earth Sciences, Faculty of Natural Sciences, University of Tabriz, 29 Bahman Boulevard, Tabriz, East Azerbaijan, Iran; Medical Geology and Environmental Research Center, Iran; Institute of Environment, University of Tabriz, Tabriz, East Azerbaijan, Iran; Traditional Medicine and Hydrotherapy Research Center, Ardabil University of Medical Sciences,Ardabil, Iran.
J Environ Manage. 2022 Feb 1;303:114168. doi: 10.1016/j.jenvman.2021.114168. Epub 2021 Dec 8.
Bayesian Model Averaging (BMA) is used to study inherent uncertainties using the Basic DRASTIC Framework (BDF) for assessing the groundwater vulnerability in a study area related to Lake Urmia. BMA is naturally an Inclusive Multiple Modelling (IMM) strategy at two levels; at Level 1 multiple models are constructed and the paper constructs three AI (Artificial Intelligence) models, which comprise ANN (Artificial Neural Network), GEP (Gene Expression Programming), and SVM (Support Vector Machines) but their outputs are fed to the next level model; at Level 2, BMA combines ANN, GEP and SVM (the Level 1 models) to quantify their inherent uncertainty in terms of within and in-between model errors. The model performance is tested by using the nitrate-N concentrations measured for the aquifer. The results show that in this particular study area, Level 1 models, even BDF, are quite accurate, but the above modelling strategy maximises the extracted information from the local data and BMA reveals that the higher uncertainties occur at areas with higher vulnerability; whereas lower uncertainties are observed at areas with lower vulnerabilities.
贝叶斯模型平均（BMA）用于使用基本 DRASTIC 框架（BDF）研究固有不确定性，以评估与乌鲁米亚湖相关研究区域的地下水脆弱性。BMA 自然是一种包容性多模型（IMM）策略，分为两个层次；在第 1 层构建多个模型，本文构建了三个人工智能（AI）模型，包括人工神经网络（ANN）、基因表达编程（GEP）和支持向量机（SVM），但它们的输出被输入到下一级模型；在第 2 层，BMA 将 ANN、GEP 和 SVM（第 1 层模型）结合起来，根据模型内和模型间误差来量化其固有不确定性。通过测量含水层中的硝酸盐-N 浓度来测试模型性能。结果表明，在这个特定的研究区域，即使是第 1 层模型，BDF 也非常准确，但上述建模策略最大限度地从本地数据中提取信息，BMA 表明，在脆弱性较高的区域不确定性较高，而在脆弱性较低的区域不确定性较低。
