Nourani Vahid, Mousavi Shahram, Sadikoglu Fahreddin, Singh Vijay P
Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran; Department of Civil Engineering, Near East University, P.O. Box: 99138, Lefkosa, North Cyprus, Mersin 10, Turkey.
Department of Water Resources Engineering, Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran.
J Contam Hydrol. 2017 Oct;205:78-95. doi: 10.1016/j.jconhyd.2017.09.006. Epub 2017 Sep 21.
This study developed a new hybrid artificial intelligence (AI)-meshless approach for modeling contaminant transport in porous media. The key innovation of the proposed approach is that both black box and physically-based models are combined for modeling contaminant transport. The effectiveness of the approach was evaluated using experimental and real world data. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were calibrated to predict temporal contaminant concentrations (CCs), and the effect of noisy and de-noised data on the model performance was evaluated. Then, considering the predicted CCs at test points (TPs, in experimental study) and piezometers (in Myandoab plain) as interior conditions, the multiquadric radial basis function (MQ-RBF), as a meshless approach which solves partial differential equation (PDE) of contaminant transport in porous media, was employed to estimate the CC values at any point within the study area where there was no TP or piezometer. Optimal values of the dispersion coefficient in the advection-dispersion PDE and shape coefficient of MQ-RBF were determined using the imperialist competitive algorithm. In temporal contaminant transport modeling, de-noised data enhanced the performance of ANN and ANFIS methods in terms of the determination coefficient, up to 6 and 5%, respectively, in the experimental study and up to 39 and 18%, respectively, in the field study. Results showed that the efficiency of ANFIS-meshless model was more than ANN-meshless model up to 2 and 13% in the experimental and field studies, respectively.
本研究开发了一种新的混合人工智能（AI）无网格方法，用于模拟多孔介质中的污染物运移。该方法的关键创新之处在于，将黑箱模型和基于物理的模型相结合来模拟污染物运移。利用实验数据和实际数据评估了该方法的有效性。对人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）进行了校准，以预测污染物的时间浓度（CCs），并评估了噪声数据和去噪数据对模型性能的影响。然后，将测试点（在实验研究中）和测压管（在米亚多阿卜平原）处预测的CCs作为内部条件，采用多二次径向基函数（MQ-RBF）这一无网格方法来求解多孔介质中污染物运移的偏微分方程（PDE），以估计研究区域内没有测试点或测压管的任何点处的CC值。利用帝国主义竞争算法确定了平流-弥散PDE中的弥散系数和MQ-RBF的形状系数的最优值。在污染物时间运移模拟中，去噪数据在实验研究中分别使ANN和ANFIS方法的决定系数性能提高了6%和5%，在现场研究中分别提高了39%和18%。结果表明，在实验研究和现场研究中，ANFIS-无网格模型的效率分别比ANN-无网格模型高2%和13%。
