This work is licensed under the Creative Commons Attribution 4.0 International License.
Abanyie S., Apea O., Abagale S., Amuah E., Sunkari E. 2023. Sources and factors influencing groundwater quality and associated health implications: A review. Emerging Contaminants, 9, 2, 100207.Search in Google Scholar
Abbas N., Wasimi S., Al-Ansari N. 2016. Assessment of climate change impacts on water resources of khabour in kurdistan, Iraq using swat model. Journal of Environmental Hydrology, 24: 716–732.Search in Google Scholar
Alameer Z., Abd Elaziz M., Ewees A., Ye H., Jianhua Z. 2019. Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61: 250–260.Search in Google Scholar
Altunkaynak A. 2007. Forecasting surface water level fluctuations of Lake Van by artificial neural networks. Water Resourources Management, 21: 399–408.Search in Google Scholar
Amuah E., Boadu A., Nandomah S. 2022. Emerging issues and approaches to protecting and sustaining surface and groundwater resources: emphasis on Ghana. Groundwater for Sustainable Development, 16, 100705.Search in Google Scholar
Andrei N. 2007. Scaled conjugate gradient algorithms for unconstrained optimization. Computational Optimization and Applications, 38: 401-416.Search in Google Scholar
Arabameri A., Pradhan B., Rezaei K., Lee S., Sohrabi M. 2020. An ensemble model for landslide susceptibility mapping in a forested area. Geocarto International, 35: 1680–1705.Search in Google Scholar
Baderna D. 2011. A combined approach to investigate the toxicity of an industrial landfill’s leachate: chemical analyses, risk assessment and in vitro assays’. Environmental Research, 111,4: 603–613.Search in Google Scholar
Banks D. 2008. An Introduction to Thermogeology: Ground Source Heating and Cooling. Blackwell Publishing Ltd., Oxford, UK.Search in Google Scholar
Bishop C.M. 1995. Neural networks for pattern recognition. Oxford University Press, Oxford.Search in Google Scholar
Bonte M., Stuyfzand P., Hulsmann A., van Beelen P. 2011. Underground thermal energy storage: environmental risks and policy developments in the Netherlands and European Union. Ecology and Society, 16, 1: 22.Search in Google Scholar
Chapman D.S., Bartlett M.G., Harris R. 2004. Comment on "Ground vs. surface air 20 temperature trends: Implications for borehole surface temperature reconstructions" by M.E. Mann and G. Schmidt. Geophysical Research Letters, 31, 7.Search in Google Scholar
Chen W., Tsangaratos P., Ilia I., Duan Z., Chen X. 2019. Groundwater spring potential mapping using population-based evolutionary algorithms and data mining methods. Science of the Total Environment, 684: 31–49.Search in Google Scholar
Czermiński P. 1993. User manual of the groundwater monitoring network in the region of the municipal landfill in Tychy-Urbanowice. EKO-SON, Tychy.Search in Google Scholar
Dąbrowska D., Witkowski A., Sołtysiak M. 2018. Application of pollution indices for the spatiotemporal assessment of the negative impact of a municipal landfill on groundwater (Tychy, southern Poland). Geological Quarterly, 62, 3: 496–508.Search in Google Scholar
Dąbrowska D., Witkowski A., Sołtysiak M. 2018. Representativeness of the groundwater monitoring results in the context of its methodology: case study of a municipal landfill complex in Poland. Environmental Earth Sciences, 77: 1–9.Search in Google Scholar
Dai Y. 2002. Convergence properties of the BFGS algoritm. SIAM Journal on Optimization, 13: 693–701.Search in Google Scholar
Gleeson T., Alley WM., Allen DM., Sophocleous MA., Zhou Y., Taniguchi M., Vandersteen J. 2012. Towards sustainable groundwater use: setting long-term goals, backcasting, and managing adaptively. Ground Water, 50: 19–26.Search in Google Scholar
Grath J., Scheidleder A., Uhlig S., Weber K., Kralik M., Keimel T., Gruber D. 2001. The EU Water Framework Directive: Statistical aspects of the identification of groundwater pollution trends and aggregation of monitoring results. Final report. Austrian Federal Ministry of Agriculture and Forestry, Environment and Water Management. European Commission, Vienna.Search in Google Scholar
Green T.R., Bates B.C., Charles S.P., Fleming P. 2007. Physically based simulation of potential effects of carbon dioxide-altered climates on groundwater recharge. Vadose Zone Journal, 6, 3: 597–609.Search in Google Scholar
Green T.R., Taniguchi M., Kooi H., Gurdak J.J., Allen D.M., Hiscock K.M., Treidel H., Aureli, A. 2011. Beneath the surface of global change: Impacts of climate change on 11 groundwater. Journal of Hydrology, 405: 532–560.Search in Google Scholar
Gunawardhana L. N., Kazama S. 2012. Statistical and numerical analyses of the influence of climate variability on aquifer water levels and groundwater temperatures: the impacts of climate change on aquifer thermal regimes. Global and Planetary Change, 86–87: 66–78.Search in Google Scholar
Gupta N., Nigam S. 2020. Crude oil price prediction using artificial neural network. Procedia Computer Science, 170: 642–647.Search in Google Scholar
Hähnlein S., Bayer P., Ferguson G., Blum P. 2013. Sustainability and policy for the thermal 19 use of shallow geothermal energy. Energy Policy, 59: 914–925.Search in Google Scholar
Heron G., Bjerg P., Gravesen P., Ludvigsen P., Christensen T. 1998. Geology and sediment geochemistry of a landfill leachate contaminated aquifer (Grinsted, Denmark). Journal of Contaminant Hydrology, 29: 301–317.Search in Google Scholar
Holman I. 2006. Climate change impacts on groundwater recharge-uncertainty, shortcomings, and the way forward? Hydrogeology Journal, 14, 5: 637–647.Search in Google Scholar
Jhariya D.C., Kumar T., Gobinath M., Diwan P., Kishore N. 2016. Assessment of groundwater potential zone using remote sensing, GIS and multi criteria decision analysis techniques. Journal of the Geological Society of India, 88, 4: 481–492.Search in Google Scholar
Jiang X., Wan L., Wang X., Ge S., Liu J. 2009. Effect of exponential decay in hydraulic conductivity with depth on regional groundwater flow. Geophysical Research Letters, 36, 24.Search in Google Scholar
Kabbour B., Zouhri L., Mainia J., Colbeaux J. 2006. Assessing groundwater contamination risk using the DASTI/IDRISI GIS method: coastal system of western Mamora, Morocco. Bulletin of Engineering Geology and Environment, 65: 463–470.Search in Google Scholar
Karlik B., Olgac A.V. 2011. Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems, 1, 4: 111–122.Search in Google Scholar
Khatami K., Khazaei B. 2014. Benefits of GIS application in hydrological modeling: a brief summary benefits of GIS application in hydrological modeling: a brief summary. VATTEN-Journal of Water Management Research, 70: 41–49.Search in Google Scholar
Lapeyre C.J., Misdariis A., Cazard N., Veynante D., Poinsot T. 2019. Training convolutional neural networks to estimate turbulent sub-grid scale reaction rates. Combustion and Flame, 203: 255–264.Search in Google Scholar
Lee S. 2018. Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”. Applied Sciences, 8, 55.Search in Google Scholar
Lee S., Hong S., Jung H. 2017. GIS-based groundwater potential mapping using artificial neural network and support vector machine models: The case of Boryeong city in Korea. Geocarto International, 33: 847–861.Search in Google Scholar
Li Y., Li J., Chen S., Diao W., 2012. Establishing indices for groundwater contamination risk assessment in the vicinity of hazardous waste landfills in China. Environmental Pollution, 165: 77–90.Search in Google Scholar
Liu J., Gao Z., Feng J., Wang M. 2023. Identification of the hydrochemical features, genesis, water quality and potential health hazards of groundwater in Dawen River Basin, North China. Ecological Indicators, 149, 110175.Search in Google Scholar
Maleki S., Nourani V., Najafi H., Hosseini Baghanam A., Ke Ch. 2023. Z-numbers based novel method for assessing groundwater specific vulnerability. Engineering Applications of Artificial Intelligence, 122, 106104, ISSN 0952-1976.Search in Google Scholar
Mato R. 1999. Environmental implications involving the establishment of sanitary landfills in five municipalities in Tanzania: the case of Tanga municipality. Resources, Conservation and Recycling 25, 1: 1–16.Search in Google Scholar
McGill B., Altchenko Y., Hamilton S., Kenabatho P., Sylvester S., Villholth K. 2019. Complex interactions between climate change, sanitation, and groundwater quality: a case study from Ramotswa, Botswana. Hydrogeology Journal, 27, 3: 997–1015.Search in Google Scholar
Mikac N., Cosovic B., Ahel S., Toncic Z. 1998. Assessment of groundwater contamination in the vicinity of a municipal waste landfill (Zagreb, Croatia). Water Sciences Technology, 37, 8: 37–44.Search in Google Scholar
Nguyen P.T., Ha D., Jaafari A., Nguyen H.D., Van Phong T., Al-Ansari N., Prakash I., Van Le H., Pham B.T. 2020. Groundwater Potential Mapping Combining Artificial Neural Network and Real AdaBoost Ensemble Technique: The DakNong Province Case-study, Vietnam. International Journal of Environment Research and Public Health, 17, 2473.Search in Google Scholar
Nielsen D.M. ed. 2006. Practical handbook of environmental site characterization and ground-water monitoring. 2nd ed. CRC Press Taylor & Francis Group: 1318.Search in Google Scholar
Nourani V., Sayyah Fard M. 2012. Sensitivity analysis of the artificial neural network outputs in simulation of the evaporation process at different climatologic regimes. Advances in Engineering Software, 47: 127–146.Search in Google Scholar
Ogretim E., Huebsch W., Shinn A. 2006. Aircraft Ice Accretion Prediction Based on Neural Networks. Journal of Aircraft, 43: 233–240.Search in Google Scholar
Panahi M., Sadhasivam N., Pourghasemi H.R., Rezaie F., Lee S. 2020. Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR). Journal of Hydrology, 588, 125033.Search in Google Scholar
Piekutowska M., Niedbała G., Piskier T., Lenartowicz T., Pilarski K., Wojciechowski T., Pilarska A., Czechowska-Kosacka A. 2021. The application of multiple linear regression and artificial neural network models for yield prediction of very early potato cultivars before harvest. Agronomy, 11, 885.Search in Google Scholar
Połap D. 2021. Fuzzy Consensus with Federated Learning Method in Medical Systems. IEEE Access, 9, 150383-150392.Search in Google Scholar
Quevauviller P., Fouillac A.M, Grath J., Ward R. 2009. Groundwater monitoring. Water Quality Measurements Series. John Willey & Sons, Ltd; 428.Search in Google Scholar
Saltelli A. 2005. Global Sensitivity analysis: An introduction. Sensitivity Analysis of Model Output, Los Alamos National Laboratory, Los Alamos.Search in Google Scholar
Saltelli A., Ratto M., Tarantola S., Campolongo F. 2005. Sensitivity analysis for chemical models. Chem Rev, 105: 2811–2828.Search in Google Scholar
Simpson S., Meixner T. 2012. Modeling effects of floods on streambed hydraulic conductivity and groundwater-surface water interactions. Water Resources Research, 48, 2Search in Google Scholar
Sitek S., Janik K., Dabrowska D., Rozkowski J., Wojtal G., Mukawa J., Witkowski A., Jakobczyk-Karpierz S. 2023. Risk assessment for the prevention of managed aquifer recharge (MAR) facility failure during the operation and the expansion phases. Journal of Hydrology, 621, 129591.Search in Google Scholar
Sitek S., Witkowski A., Kowalczyk A., Żurek – Pucek A. 2010. Impact assessment of municipal landfill in Tychy on groundwater environment – modelling study. Biuletyn Państwowego Instytutu Geologicznego, 442: 147–152.Search in Google Scholar
Turan V., Aydın S., Sönmez O. 2022. Production, Cost Analysis, and Marketing of Bioorganic Liquid Fertilizers and Plant Nutrition Enhancers. [in:] N. Amaresan, D. Dharumadurai, D.R. Cundell (Eds.), Industrial Microbiology Based Entrepreneurship. Microorganisms for Sustainability, 42, Springer, Singapore: 193–198.Search in Google Scholar
Venkatesan P., Anitha S. 2006. Application of a radial basis function neural network for diagnosis of diabetes mellitus. Current Science, 91, 9: 1195–1199.Search in Google Scholar
Verma S., Thampi G., Rao M. 2020. Ann based method for improving gold price forecasting accuracy through modified gradient descent methods. IAES International Journal of Artificial Intelligence, 9, 46: 46–57.Search in Google Scholar
Weber W.J., Jang W., Townsend T., Laux S. 2002. Leachate from land disposed residential construction waste. Journal of Environmental Engineering, 128, 3: 237–245.Search in Google Scholar
Werner A.D., Jakovovid D., Simmons C.T. 2009. Experimental observations of saltwater up-coning. Journal of Hydrology, 373: 230–241.Search in Google Scholar
Witczak S., Kania J., Kmiecik E. 2013. Catalogue of the Selected Physical and Chemical Indicators of Groundwater Contamination and Methods of Their Determination. The Library of the Environmental Monitoring, Warsaw.Search in Google Scholar
Witkowski A., Rubin K., Kowalczyk A., Różkowski A., Wróbel J. 2003. Groundwater vulnerability map of the Chrzanów karst-fissured Triassic aquifer (Poland). Environmental Geology, 4, 1: 59–67.Search in Google Scholar
Witkowski A.J. 2008. Groundwater quality monitoring for the post-exploitation phase of the municipal landfill site in Tychy-Urbanowice. Archive of Intergeo Ltd.Search in Google Scholar
Witkowski A.J. 2019. Groundwater monitoring in the region of the municipal landfill in Tychy-Urbanowice – Reports 2018. University of Silesia, Sosnowiec.Search in Google Scholar
Witkowski A.J. 2023. Groundwater monitoring in the region of the municipal landfill in Tychy-Urbanowice – Reports 2022. University of Silesia, Sosnowiec.Search in Google Scholar
Wos A. 2010. Climate of Poland in the second half of the 20th century. Wydawnictwo Naukowe UAM, Poznan: 1–489.Search in Google Scholar
Xiao J., Wang L., Chai N., Liu T., Jin Z., Rinklebe J. 2021. Groundwater hydrochemistry, source identification and pollution assessment in intensive industrial areas, eastern Chinese Loess Plateau. Environmental Pollution, 278: 116930.Search in Google Scholar
Zhang P., Ci B. 2020. Deep belief network for gold price forecasting. Resources Policy, 69: 101806.Search in Google Scholar
Zhang Q., Qian H., Xu P., Hou K., Yang F. 2021. Groundwater quality assessment using a new integrated-weight water quality index (IWQI) and driver analysis in the Jiaokou Irrigation District, China. Ecotoxicology and Environmental Safety, 212: 111992.Search in Google Scholar
, 