Modeling prediction of dispersal of heavy metals in plain using neural network

References

• Arlai P, Koch M. 2017. Density-dependent numerical simulations of the impact of underground dams on groundwater storage on Samui island, Thailand. J Appl Water Eng Res. 5(2):114–128. doi: 10.1080/23249676.2016.1171734
   Web of Science ®Google Scholar
• Balkhair KS, Ashraf MA. 2016. Field accumulation risks of heavy metals in soil and vegetable crop irrigated with sewage water in western region of Saudi Arabia. Saudi J Biol Sci. 23(1):S32–S44. doi: 10.1016/j.sjbs.2015.09.023
   PubMed Web of Science ®Google Scholar
• Bear J, Verruijt A. 2012. Modeling groundwater flow and pollution. Netherlands: Springer Science & Business Media 2.
   Google Scholar
• Carlin DJ, Naujokas MF, Bradham KD, Cowden J, Heacock M, Henry HF, Lee JS, Thomas DJ, Thompson C, Tokar EJ, Waalkes MP. 2016. Arsenic and environmental health: state of the science and future research opportunities. Environ Health Perspect. 124(7):890–899. doi: 10.1289/ehp.1510209
   PubMed Web of Science ®Google Scholar
• Cigizoglu HK. 2008. Artificial neural networks in water resources. In: Integration of information for environmental security. Dordrecht: Springer; p. 115–148.
   Google Scholar
• Fetter CW, Boving T, Kreamer D. 2017. Contaminant hydrogeology. Long Grove (IL): Waveland Press.
   Google Scholar
• Goodfellow I, Bengio Y, Courville A, Bengio Y. 2016. Deep learning. Cambridge: MIT Press.
   Google Scholar
• Govindaraju RS. 2000. Artificial neural networks in hydrology. I: preliminary concepts. J Hydrol Eng. 5(2):115–123. doi: 10.1061/(ASCE)1084-0699(2000)5:2(115)
   Web of Science ®Google Scholar
• Hung NQ, Babel MS, Weesakul S, Tripathi NK. 2009. An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol Earth Syst Sci. 13(8):1413–1425. doi: 10.5194/hess-13-1413-2009
   Web of Science ®Google Scholar
• Jiang H, Cotton WR. 2004. Soil moisture estimation using an artificial neural network: a feasibility study. Can J Remote Sensing. 30(5):827–839. doi: 10.5589/m04-041
   Web of Science ®Google Scholar
• Kalantari, N., Sajadi, Z., Makvandi, M. and Keshavarzi, M. 2012. Chemical properties of soil and groundwater of the Assaluyeh alluvial plain with emphasis on heavy metals contamination.
   Google Scholar
• Kothe SA. 2015. Development of rainfall–runoff model using artificial neural network for a Priyadarshini watershed [PhD diss.] College of Agricultural Engineering and Technology.
   Google Scholar
• Lee S, Cho S, Wong PM. 1998. Rainfall prediction using artificial neural networks. J Geog Inform Decision Anal. 2(2):233–242.
   Google Scholar
• Mahmood A, Malik RN. 2014. Human health risk assessment of heavy metals via consumption of contaminated vegetables collected from different irrigation sources in Lahore, Pakistan, Arabian. J Chem. 7(1):91–99.
   Web of Science ®Google Scholar
• Menendez AN, Lecertúa EA, Badano ND, García PE. 2016. Numerical modeling to define remediation actions for water quality in streams. J Appl Water Eng Res. 4(1):67–81. doi: 10.1080/23249676.2015.1072850
   Web of Science ®Google Scholar
• Moasheri SA, Tabatabaie SM. 2013. Estimating the groundwater nitrate by using artificial neural network and optimizing it by genetic algorithm. Int J Agric. 3(4):699.
   Google Scholar
• Nguyen DD. 2017. Designing optimal knowledge base for neural expert systems. Int Bus Res. 10(6):137. doi: 10.5539/ibr.v10n6p137
   Google Scholar
• Ostad-Ali-Askari K, Shayannejad M, Ghorbanizadeh-Kharazi H. 2017. Artificial neural network for modeling nitrate pollution of groundwater in marginal area of Zayandeh-rood River, Isfahan, Iran. KSCE J Civil Eng. 21(1):134–140. doi: 10.1007/s12205-016-0572-8
   Web of Science ®Google Scholar
• Rajanayaka C, Samarasinghe S, Kulasiri D. 2002. Solving the inverse problem in stochastic groundwater modelling with artificial neural networks. 1st International Congress on Environmental Modelling and Software; Jun; Lugano, Switzerland.
   Google Scholar
• Sagnak C. 1991. Groundwater pollution originated from GEOLOGICAL formation: example of Konya–Cumra–Karapinar plain with GIS application. Department of Geotechnical Services DSI, Ankara, Turkey.
   Google Scholar
• Selvakumar S, Ramkumar K, Chandrasekar N, Magesh NS, Kaliraj S. 2017. Groundwater quality and its suitability for drinking and irrigational use in the Southern Tiruchirappalli district, Tamil Nadu, India. Appl Water Sci. 7(1):411–420. doi: 10.1007/s13201-014-0256-9
   Web of Science ®Google Scholar
• Shamim MA, Ghumman AR, Ghani U. 2004. Forecasting groundwater contamination using artificial neural networks. Paper presented at 1st International Conference on Water Resources and Arid Environments, 5–8 December.
   Google Scholar
• Smith BA, McClendon RW, Hoogenboom G. 2006. Improving air temperature prediction with artificial neural networks. Int J Comput Intell. 3(3):179–186.
   Google Scholar
• Sudheer KP, Gosain AK, Mohana Rangan D, Saheb SM. 2002. Modelling evaporation using an artificial neural network algorithm. Hydrol Processes. 16(16):3189–3202. doi: 10.1002/hyp.1096
   Web of Science ®Google Scholar
• Tiwari MK, Bajpai S, Dewangan UK, Tamrakar RK. 2015. Assessment of heavy metal concentrations in surface water sources in an industrial region of central India. Karbala Int J Mod Sci. 1(1):9–14. doi: 10.1016/j.kijoms.2015.08.001
   Google Scholar
• Turban E, Sharda R, Delen D. 2011. Decision support and business intelligence systems. Upper Saddle River, NJ: Pearson Education Inc.
   Google Scholar
• Vallero D. 2010. Environmental contaminants: assessment and control. New York: Cornell University.
   Google Scholar
• Vinyals O, Le Q. 2015. A neural conversational model. arXiv preprint arXiv:1506.05869.
   Google Scholar
• Wongsasuluk P, Chotpantarat S, Siriwong W, Robson M. 2014. Heavy metal contamination and human health risk assessment in drinking water from shallow groundwater wells in an agricultural area in Ubon Ratchathani province, Thailand. Environ Geochem Health. 36(1):169–182. doi: 10.1007/s10653-013-9537-8
   PubMed Web of Science ®Google Scholar
• Yoon H, Hyun Y, Lee KK. 2007. Forecasting solute breakthrough curves through the unsaturated zone using artificial neural networks. J Hydrol. 335(1–2):68–77. doi: 10.1016/j.jhydrol.2006.11.001
   Web of Science ®Google Scholar
• Zemzami M. 2016. Flow forecasts in a non-perennial river of an arid basin using neural networks. J Appl Water Eng Res. 4(2):92–101. doi: 10.1080/23249676.2015.1072851
   Web of Science ®Google Scholar
• Zhang Z. 2018. Multivariate time series analysis in climate and environmental research. New York: Springer.
   Google Scholar