The impact of weather conditions on the quality of groundwater in the area of a municipal waste landfill
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DOI: 10.2478/environ-2023-0013
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- Abdüsselam Altunkaynak, 2007. "Forecasting Surface Water Level Fluctuations of Lake Van by Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(2), pages 399-408, February.
- McGill, B. M. & Altchenko, Yvan & Hamilton, S. K. & Kenabatho, P. K. & Sylvester, S. R. & Villholth, Karen G., 2019. "Complex interactions between climate change, sanitation, and groundwater quality: a case study from Ramotswa, Botswana," Papers published in Journals (Open Access), International Water Management Institute, pages 27(3):997-1.
- Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
- Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
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Keywords
hydrogeology; landfill; artificial neural networks; MLP; Tychy; Poland;All these keywords.
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