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Prediction of the Seasonal Changes of the Chloride Concentrations in Urban Water Reservoir

Abstract

This study investigated the possibility of using artificial neural networks to predict changes in the concentration of chloride ions in the urban ponds on the example of the inflow and outflow zones of water to and from the ponds Syrenie Stawy in Szczecin (NW-Poland). The possibility of using selected water quality indices (selected based on correlation matrix of water quality indices with Cl), in particular: COD-Cr, BOD5, DO, water saturation by O2 and NO2 and their influence on the chloride concentration forecast was tested.

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Application of neural networks to the prediction of gas pollution of air

water quality prediction. Polish Journal of Environmental Studies, 21(5A), pp. 271-275. Pawul, M. and Śliwka, M. (2016). Application of artificial neural networks for prediction of air pollution levels in environmental monitoring. Journal of Ecological Engineering 17, pp.190-196. Rybarczyk, Y. and Zalakeviciute, R. (2018). Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review. Applied Sciences, 8, 2570. smog.imgw.pl, (2019). IMGW Official Website. [online] Available at http://smog.imgw.pl/content/model [Accessed 10 Apr

Open access
Application of Selected Methods of Black Box for Modelling the Settleability Process in Wastewater Treatment Plant

.1007/BF00337288. [17] Han HG, Chen QL, Qiao JF. An efficient self-organizing RBF neural network for water quality prediction. Neural Network. 2011;24(7):717-725. DOI: 10.1016/j.neunet.2011.04.006. [18] Han HG, Ying L, Guo YN, Qiao JF. A soft computing method to predict sludge volume index based on a recurrent self-organizing neural network. J Appl Soft Computing. 2016;38(C):477-486. DOI: 10.1016/j.asoc.2015.09.051. [19] Martins AMP, Heijnen JJ, van Loosdrecht MCM. Bulking sludge in biological nutrient removal systems

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Prediction of Water Quality in Riva River Watershed

statistical methods and pollution indicators. J Soil Water Conserv. 2019;7:47-56. DOI: 10.1016/j.iswcr.2018.09.001. [18] Papazova P, Simeonova P. Long-term statistical assessment of the river quality of Tundja River. Ecol Chem Eng S. 2012;19:213-26. DOI: 10.2478/v10216-011-0016-9. [19] Avila R, Horn B, Moriarty E, Hodson R, Moltchanova E. Evaluating statistical model performance in water quality prediction. J Environ Manage. 2018;206:910-9. DOI: 10.1016/j.jenvman.2017.11.049. [20] Libera DA, Sankarasubramanian A. Multivariate bias corrections of

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Forest Fragmentation Analysis from Multiple Imaging Formats

. (2016). Forests of Virginia: Importance, Composition, Ecology, Threats, and Management. Virginia Master Naturalist Basic Training Course: Forests of Virginia. Retrieved August 25, 2018, from https://pubs.ext.vt.edu/content/dam/pubs_ext_vt_edu/465/465-315/465-315_pdf.pdf . Garcia-Gigorro, S. and Saura, S. (2005). Forest fragmentation estimated from remotely sensed data: is comparison across scales possible? Forest Science . 51: 51–63. Geza, M. and McCray, J. E. (2008). Effects of soil data resolution on SWAT model stream flow and water quality

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