Open Access
Issue
E3S Web Conf.
Volume 118, 2019
2019 4th International Conference on Advances in Energy and Environment Research (ICAEER 2019)
Article Number 03018
Number of page(s) 5
Section Environment Engineering, Environmental Safety and Detection
DOI https://doi.org/10.1051/e3sconf/201911803018
Published online 04 October 2019
  1. R. B. E. Shutes, Artificial wetlands and water quality improvement, Environ. Int., 26, 5, 441-447 (2001) [CrossRef] [PubMed] [Google Scholar]
  2. C. Makropoulos and D. Butler, A multi-objective evolutionary programming approach to the ‘object location’spatial analysis and optimisation problem within the urban water management domain, Civ. Eng. Environ. Syst., 22, 2, 85-101 (2005) [CrossRef] [Google Scholar]
  3. C. H. Mortimer, Chemical exchanges between sediments and water in the great lakes‐speculations on probable regulatory mechanisms, Limnol. Oceanogr., 16, 2, 387-404 (1971) [Google Scholar]
  4. H.-B. Moon, M. Choi, J. Yu, R.-H. Jung, and H.-G. Choi, Contamination and potential sources of polybrominated diphenyl ethers (PBDEs) in water and sediment from the artificial Lake Shihwa, Korea, Chemosphere, 88, 7, 837-843 (2012) [CrossRef] [Google Scholar]
  5. J. Su, X. Wang, S. Zhao, B. Chen, C. Li, and Z. Yang, A structurally simplified hybrid model of genetic algorithm and support vector machine for prediction of chlorophyll a in reservoirs, Water, 7, 4, 1610-1627 (2015) [Google Scholar]
  6. S. Razavi, B. A. Tolson, and D. H. Burn, Review of surrogate modeling in water resources, Water. Resour. Res., 48, 7 (2012) [Google Scholar]
  7. R. S. Govindaraju and A. R. Rao, Artificial neural networks in hydrology. (Springer Science & Business Media, 2013) [Google Scholar]
  8. S. Mohanty, M. K. Jha, S. Raul, R. Panda, and K. Sudheer, Using artificial neural network approach for simultaneous forecasting of weekly groundwater levels at multiple sites, Water. Resour. Manag., 29, 15, 5521-5532 (2015) [CrossRef] [Google Scholar]
  9. M. Behzad, K. Asghari, M. Eazi, and M. Palhang, Generalization performance of support vector machines and neural networks in runoff modeling, Expert. Syst. Appl., 36, 4, 7624-7629 (2009) [Google Scholar]
  10. K. Ostad-Ali-Askari, M. Shayannejad, and H. Ghorbanizadeh-Kharazi, Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran, Ksce. J. Civ. Eng., 21, 1, 134-140 (2017) [CrossRef] [Google Scholar]
  11. V. Bedekar, E. D. Morway, C. D. Langevin, and M. J. Tonkin, MT3D-USGS version 1: A US Geological Survey release of MT3DMS updated with new and expanded transport capabilities for use with MODFLOW, (US Geological Survey, 2016) [Google Scholar]
  12. C. Zheng, MT3D: A modular three-dimensional transport model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems, (SS Papadopulos & Associates, 1992) [Google Scholar]
  13. C. Zheng and P. P. Wang, MT3DMS: a modular three-dimensional multispecies transport model for simulation of advection, dispersion, and chemical reactions of contaminants in groundwater systems; documentation and user’s guide, (Alabama Univ University, 1999) [Google Scholar]

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