Determination of relationship between hardness and groundwater quality parameters by neural networks

References

• Balkaya , N. 1989 . Groundwater pollution investigation between Incesu Derekoy plain at Samsun Region , Samsun , , Turkey : Ondokuz Mayis University, Natural Science Institute . Master Thesis
   Google Scholar
• Coynel , A. , Schafer , J. , Dabrin , A. , Girardot , N. and Blanc , G. 2007 . Groundwater contributions to metal transport in a small river affected by mining and smelting waste . Water Res. , 41 : 3420 – 3428 .
   PubMed Web of Science ®Google Scholar
• Furman , A. and Yenigun , O. 1998 . The environmental dimension , 2nd edn , Istanbul : Bogazici University press .
   Google Scholar
• Wahab-Abdul , S. A. and Al-Alawi , S. M. 2002 . Assesment and prediction of trospheric ozone concentration levels using artifical neural networks . Environ. Model. Softw. , 17 : 219 – 228 .
   Web of Science ®Google Scholar
• Chakraborty , K. , Mehrotra , K. , Mohan , C. K. and Ranka , S. 1992 . Forecasting the behavior of multivariate time series using neural networks . Neural Netw. , 5 : 961 – 970 .
   Web of Science ®Google Scholar
• Tang , Z. , De Almeida , C. and Fishwick , P. A. 1991 . Time series forecasting using neural networks vs. Box-Jenkins methodology . Simulation , 57 ( 5 ) : 303 – 310 .
   Google Scholar
• Clair , T. A. and Ehrman , J. M. 1996 . Variations in discharge and dissolved organic carbon and nitrogen export from terrestrial basins with changes in climate: A neural network approach . Limnol. Uceanogr. , 41 ( 5 ) : 921 – 927 .
   Web of Science ®Google Scholar
• Desilets , L. , Golden , B. , Wang , Q. and Kumar , R. 1992 . Predicting salinity in the Chesapeake Bay using backpropagation . Comput. Oper. Res. , 19 ( 3/4 ) : 227 – 285 .
   Web of Science ®Google Scholar
• Comrie , A. C. 1997 . Comparing neural networks and regression models for ozone forecasting . J. Air Waste Manage. Assoc. , 47 ( 6 ) : 653 – 663 .
   Web of Science ®Google Scholar
• Ozcan , H. K. , Sahin , U. , Ucan , O. N. and Bayat , C. 2006 . Modelling Of Trophospheric Ozone (O3) Concentrations Using Cellular Neural Networks . J. Fac. Eng. Arch. Gazi Univ. , 21 ( 2 ) : 239 – 245 .
   Google Scholar
• Boznar , M. , Lesjak , M. and Mlakar , P. 1993 . A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain . Atmos. Environ. , 27 B ( 2 ) : 221 – 230 .
   Web of Science ®Google Scholar
• Ozcan , H. K. , Ucan , O. N. , Sahin , U. , Borat , M. and Bayat , C. 2006 . Artificial neural network modeling of methane emissions at Istanbul Kemerburgaz-Odayeri Landfill Site . J. Sci. Ind. Res. , 65 ( 2 ) : 128 – 134 .
   Web of Science ®Google Scholar
• Paruelo , J. M. and Tomasel , F. 1997 . Prediction of functional characteristics of ecosystem, A comparison of artificial neural networks and regression models . Ecol. Model. , 98 ( 2/3 ) : 173 – 186 .
   Web of Science ®Google Scholar
• Whitehead , P. G. , Howard , A. and Arulmani , C. 1997 . Modeling algal growth and transport in rivers-A comparison of time series analysis, dynamic mass balance and neural network techniques . Hydrobiologia , 349 : 39 – 46 .
   Web of Science ®Google Scholar
• Gencay , R. and Liu , T. 1997 . Nonlinear modelling and prediction with feedforward and recurrent networks . Physica , D 108 ( l/2 ) : 119 – 134 .
   Google Scholar
• Krishnapura , V. G. and Jutan , A. 1997 . ARMA neuron networks for modeling nonlinear dynamical systems . Can. J. Chem. Eng. , 76 ( 3 ) : 574 – 582 .
   Google Scholar
• Udono , K. and Site , R. 2008 . Modeling seawater desalination powered by waste incineration using a dynamic systems approach . Desalination , 229 : 302 – 317 .
   Web of Science ®Google Scholar
• Lin , T. , Horne , B. G. , Tino , P. and Giles , C. L. 1996 . Learning long-term dependencies in NARX recurrent neural networks . IEEE Trans. Neural Netw. , 7 ( 6 ) : 1329 – 1338 .
   PubMed Web of Science ®Google Scholar
• Siegehmann , H. T. , Horne , B. G. and Giles , C. L. 1997 . Computational capabilities of recurrent NARX neural networks . IEEE Trans. Syst. Man Cyb. , B 27 ( 2 ) : 208 – 215 .
   Web of Science ®Google Scholar
• Maier , H. R. and Dandy , G. C. 2001 . Neural Network based modeling of environmental variables: A systematic approach . Math. Computer Modeling , 33 : 669 – 682 .
   Web of Science ®Google Scholar
• APHA-AWWA-WPCF . 1989 . Standard Methods for the Examination of Water and Wastewater , 17th edition New York
   Google Scholar
• Anitha , M. and Singh , H. 2008 . Artificial neural network simulation of rare earths solvent extraction equilibrium data . Desalination , 232 : 59 – 70 .
   Web of Science ®Google Scholar
• Viotti , P. , Liuti , G. and Di Genova , P. 2002 . Atmospheric urban pollution: Applications of an artificial neural network (ANN) to the city of Perugia . Ecol Model. , 148 : 27 – 46 .
   Web of Science ®Google Scholar