کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5765826 1627006 2017 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Experimental and AI-based numerical modeling of contaminant transport in porous media
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات فرآیندهای سطح زمین
پیش نمایش صفحه اول مقاله
Experimental and AI-based numerical modeling of contaminant transport in porous media
چکیده انگلیسی


- Hybrid black box-physical-based model was developed for contaminant transport.
- AI and MQ-RBF meshless methods used respectively for temporal and spatial modeling.
- Prior to a field study, model was verified by laboratory experiments.
- De-noised data enhanced the performance of the hybrid AI-meshless model.
- Efficiency of ANFIS-meshless model was superior to ANN-meshless model up to 13%.

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Contaminant Hydrology - Volume 205, October 2017, Pages 78-95
نویسندگان
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