[[1] Hotloś H. Ilościowa ocena wpływu wybranych czynników na parametry i koszty eksploatacyjne sieci wodociągowych (Quantitative effect assessment of selected factors on indicators and operating costs of water-pipe networks). Wrocław: Ofic Wyd Politechniki Wrocławskiej; 2007. http://www.dbc.wroc.pl/Content/4273/Hotlos.pdf.]Search in Google Scholar
[[2] Ladopoulos EG. Non-linear real-time expert water management telematics system for leaks control. Water Res. 2013;40:476:482. DOI: 10.1134/S0097807813040076.10.1134/S0097807813040076]Search in Google Scholar
[[3] Hoang TH, Mouton A, Lock K, De Pauw N, Goethals PLM. Integrating data-driven ecological models in an expert-based decision support system for water management in the Du river basin (Vietnam). Environ Monit Assess. 2013;185:631-642. DOI: 10.1007/s10661-012-2580-6.10.1007/s10661-012-2580-6]Search in Google Scholar
[[4] Cretescu I, Craciun I, Benchea RE, Kovács Z, Iavorschi A, Sontea V, et al. Development of an expert system for surface water quality monitoring in the context of sustainable management of water resources. Environ Eng Manage J. 2013;12:1721-1734. http://omicron.ch.tuiasi.ro/EEMJ/pdfs/vol12/no8/20_769_Cretescu_13.pdf.10.30638/eemj.2013.206]Search in Google Scholar
[[5] Nicklow J, Reed P, Savic D, Dessalegne T, Harrell L, Chan-Hilton A, et al. State of the art for genetic algorithms and beyond in water resources planning and management. J Water Resources Plann Manage. 2010;136:412-432. DOI: 10.1061/(ASCE)WR.1943-5452.0000053.10.1061/(ASCE)WR.1943-5452.0000053]Search in Google Scholar
[[6] Tchorzewska-Cieslak B. Matrix method for estimating the risk of failure in the collective water supply system using fuzzy logic. Environ Protect Eng. 2011;38:111-118. http://epe.pwr.wroc.pl/2011/3_2011/12tchorzewska.pdf.]Search in Google Scholar
[[7] Kolasa-Więcek A. Use of artificial neural networks in predicting direct nitrous oxide emissions from agricultural soils. Ecol Chem Eng S. 2013;20:419-428. DOI: 10.2478/eces-2013-0030.10.2478/eces-2013-0030]Search in Google Scholar
[[8] Olawoyin R. Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil. Chemosphere. 2016;161:145-150. DOI: 10.1016/j.chemosphere.2016.07.003.10.1016/j.chemosphere.2016.07.00327424056]Search in Google Scholar
[[9] Wu Y, Wang J. A novel hybrid model based on artificial neural networks for solar radiation prediction. Renew Energy. 2016;89:268-284. DOI: 10.1016/j.renene.2015.11.070.10.1016/j.renene.2015.11.070]Search in Google Scholar
[[10] Selami D, Karadeniz A, Demir NM. Using steepness coefficient to improve artificial neural network performance for environmental modeling. Pol J Environ Stud. 2016;25:1467-1477. DOI: 10.15244/pjoes/61958.10.15244/pjoes/61958]Search in Google Scholar
[[11] Wang Y, Yu T-Y. Novel tornado detection using an adaptive neuro-fuzzy system with S-band polarimetric weather radar. J Atmosph Oceanic Technol. 2015;32:195-208. DOI: 10.1175/JTECH-D-14-00096.1.10.1175/JTECH-D-14-00096.1]Search in Google Scholar
[[12] Kaur G. Neural networks to identify tornadic/nontornadic circulations based on various radar attributes. Intern J Sci Eng Res. 2013;4:1124-1126. http://www.ijser.org/researchpaper%5CNeural-Networks-toidentify-Tornadic-NonTornadic-Circulations-based-on-various-radar-attributes.pdf.]Search in Google Scholar
[[13] Liu Y, Xia J, Shi C-X, Hong Y. An improved cloud classification algorithm for China’s FY-2C multi-channel images using artificial neural network. Sensors. 2009;9:5558-5579. DOI: 10.3390/s90705558.10.3390/s90705558327413622346714]Search in Google Scholar
[[14] Kuril S, Saini I, Saini BS. Cloud classification for weather information by artificial neural network. International J Appl Phys Math. 2013;3:28-30. DOI: 10.7763/IJAPM.2013.V3.167.10.7763/IJAPM.2013.V3.167]Search in Google Scholar
[[15] Asadollahfardi G, Zangooei H, Aria SH. Predicting PM2.5 concentrations using artificial neural networks and Markov chain, a case study Karaj City. Asian J Atmosph Environ. 2016;10:67-79. DOI: 10.5572/ajae.2016.10.2.067.10.5572/ajae.2016.10.2.067]Search in Google Scholar
[[16] Kaminski W, Tomczak E. An integrated neural model for drying and thermal degradation of selected products. Drying Technol. 1999, 17:7-8, 1291-1301. DOI: 10.1080/07373939908917615.10.1080/07373939908917615]Search in Google Scholar
[[17] Kolasa-Więcek A. Exploitation of water resources of the Opole province - forecasting with the use of artificial neural networks. Ecol Chem Eng S. 2010;17:363-371. http://tchie.uni.opole.pl/freeECE/S_17_3/KolasaWiecek_17(S3).pdf.]Search in Google Scholar
[[18] Korus I, Piotrowski K. Neural network model prediction of chromium separation in polyelectrolyteenhanced ultrafiltration. Ecol Chem Eng A. 2014;21:377-385. DOI: 10.2428/ecea.2014.21(3)31.]Search in Google Scholar
[[19] Sentas A, Psilovikos A, Psilovikos T, Matzafleri N. Comparison of the performance of stochastic models in forecasting daily dissolved oxygen data in dam-Lake Thesaurus. Desalin Water Treatm. 2016;57:11660-11674. DOI: 10.1080/19443994.2015.1128984.10.1080/19443994.2015.1128984]Search in Google Scholar
[[20] Lee JHW, Huang Y, Dickman M, Jayawardena AW. Neural network modelling of coastal algal blooms. Ecol Modelling. 2003;159:179-201. DOI: 10.1016/S0304-3800(02)00281-8.10.1016/S0304-3800(02)00281-8]Search in Google Scholar
[[21] Wei B, Sugiura N, Maekawa T. Use of artificial neural network in the prediction of algal blooms. Water Res. 2001;35:2022-2028. DOI: 10.1016/S0043-1354(00)00464-4.10.1016/S0043-1354(00)00464-4]Search in Google Scholar
[[22] Możejko J, Gniot R. Application of neural networks for the prediction of total phosphorus concentrations in surface waters. Pol J Environ Stud. 2008;17:363-368. http://www.pjoes.com/pdf/17.3/363-368.pdf.]Search in Google Scholar
[[23] Kutyłowska M. Neural network approach for failure rate prediction. Eng Failure Anal. 2015;47:41-48. DOI: 10.1016/j.engfailanal.2014.10.007.10.1016/j.engfailanal.2014.10.007]Search in Google Scholar
[[24] Kutyłowska M. Prediction of water conduits failure rate - comparison of support vector machine neural network. Ecol Chem Eng A. 2016;23:147-160. DOI: 10.2428/ecea.2016.23(2)11.]Search in Google Scholar
[[25] Rojek I, Studziński J. Sieci neuronowe w lokalizacji awarii w sieci wodociągowej (Application of neuronal networks for localization of a failure in the water supply network). Studia i Materiały Informatyki Stosowanej. 2012;9:29-34. http://repozytorium.ukw.edu.pl/handle/item/3539.]Search in Google Scholar
[[26] Cieżak W, Siwoń Z, Cieżak J. Zastosowanie sztucznych sieci neuronowych do prognozowania szeregów czasowych krótkotrwałego poboru wody w wybranych systemach wodociągowych (Artificial neural networks for predicting time series of water demand in selected municipal water supply systems). Ochrona Środ. 2006:39-44. http://www.os.not.pl/docs/czasopismo/2006/Ciezak_1-2006.pdf.]Search in Google Scholar
[[27] Muszyński K. Metoda sztucznych sieci neuronowych w prognozowaniu bieżącym zapotrzebowania na wodę w Krakowie (Artificial neural network method in current prediction of water demand in Krakow), Rozprawa doktorska (PhD Thesis). Kraków: Politechnika Krakowska im. Tadeusza Kościuszki; 2012. https://suw.biblos.pk.edu.pl/downloadResource&mId=979033.]Search in Google Scholar
[[28] Dawidowicz J. Ocena średnic przewodów wodociągowych za pomocą sieci neuronowych Kohonena (Evaluation of water pipe diameters using Kohonen neural networks). J Civil Eng, Environ Architect. 2015;62:43-64. DOI: 10.7862/rb.2015.4.10.7862/rb.2015.4]Search in Google Scholar
[[29] Kamiński W, Strumiłło P, Tomczak E. Zastosowanie sztucznej inteligencji w rozwiązywaniu wybranych problemów ochrony atmosfery (Application of artificial intelligence systems for solving some environmental problems). Łódź: PAN Oddział w Łodzi; 2005.]Search in Google Scholar
[[30] Osowski S. Sieci neuronowe w ujęciu algorytmicznym (An algorithmic approach to neural networks). Warszawa: WNT; 1996.]Search in Google Scholar