A comparative analysis among computational intelligence techniques for dissolved oxygen prediction in Delaware River
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Ahmed, 2013, Application of artificial neural network models for predicting dissolved oxygen concentration for Surma River, Bangladesh, Journal of Applied Technology in Environmental Sanitation, 3, 135
Altun, 2007, Treatment of multi-dimensional data to enhance neural network estimators in regression problems, Expert Systems with Applications, 32, 599, 10.1016/j.eswa.2006.01.054
Antanasijević, 2013, Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study, Environmental Science and Pollution Research, 20, 9006, 10.1007/s11356-013-1876-6
Aqil, 2007, A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff, Journal of Hydrology, 337, 22, 10.1016/j.jhydrol.2007.01.013
ASCE Task Committee on the application of ANNs in hydrology, 2000, Artificial neural networks in hydrology, I: preliminary concepts, Journal of Hydrologic Engineering, 5, 115, 10.1061/(ASCE)1084-0699(2000)5:2(115)
Ay, 2012, Modeling of dissolved oxygen concentration using different neural network techniques in Foundation Creek, El Paso County, Colorado, USA, Journal of Environmental Engineering, 138, 654, 10.1061/(ASCE)EE.1943-7870.0000511
Babovic, 2002, Rainfall runoff modeling based on genetic programming, Nordic Hydrology, 33, 331, 10.2166/nh.2002.0012
Babovic, 2000, Genetic programming as a model induction engine, Journal of Hydroinformatics, 2, 35, 10.2166/hydro.2000.0004
Boano, 2006, Stochastic modelling of DO and BOD components in a stream with random inputs, Advances in Water Resources, 29, 1341, 10.1016/j.advwatres.2005.10.007
Brameier, 2001, A comparison of linear genetic programming and neural networks in medical data mining, IEEE Transactions on Evolutionary Computation, 5, 17, 10.1109/4235.910462
Brameier, 2007
Bray, 2004, Identification of support vector machines for runoff modelling, Journal of Hydroinformatics, 6, 265, 10.2166/hydro.2004.0020
Chen, 2014, Artificial neural network modeling of dissolved oxygen in reservoir, Environmental Monitoring and Assessment, 186, 1203, 10.1007/s10661-013-3450-6
Cimen, 2008, Estimation of daily suspended sediments using support vector machines, Hydrological Sciences Journal, 53, 656, 10.1623/hysj.53.3.656
Danandeh Mehr, 2013, Streamflow prediction using linear genetic programming in comparison with a neuro-wavelet technique, Journal of Hydrology, 505, 240, 10.1016/j.jhydrol.2013.10.003
Danandeh Mehr, 2014, Linear genetic programming application for successive-station monthly streamflow prediction, Computers & Geosciences, 70, 63, 10.1016/j.cageo.2014.04.015
Danandeh Mehr, 2014, A gene-wavelet model for long lead-time drought forecasting, Journal of Hydrology, 517, 691, 10.1016/j.jhydrol.2014.06.012
Danandeh Mehr, 2014, Successive-station monthly streamflow prediction using different ANN algorithms, International Journal of Environmental Science and Technology, 12, 2191, 10.1007/s13762-014-0613-0
Dibike, 2001, Model induction with support vector machines: introduction and applications, Journal of Computing in Civil Engineering, 15, 208, 10.1061/(ASCE)0887-3801(2001)15:3(208)
Fernando, 1998, Runoff forecasting using RBF networks with OLS algorithm, Journal of Hydrologic Engineering, 3, 203, 10.1061/(ASCE)1084-0699(1998)3:3(203)
Fletcher, 1993, Forecasting with neural networks: an application using bankruptcy data, Journal of Information Management, 24, 159, 10.1016/0378-7206(93)90064-Z
Gao, 2001, A probabilistic framework for SVM regression and error bar estimation, Machine Learning, 46, 71, 10.1023/A:1012494009640
Garcia, 2002, A model for predicting the temporal evolution of dissolved oxygen concentration in shallow estuaries, Hydrobiology, 475–476, 205, 10.1023/A:1020365225564
Ghorbani, 2010, Sea water level forecasting using genetic programming and artificial neural networks, Computers & Geosciences, 36, 620, 10.1016/j.cageo.2009.09.014
Gunn, 1998, 66
Guven, 2009, Linear genetic programming for time-series modeling pf daily flow rate, Journal of Earth System Science, 118, 137, 10.1007/s12040-009-0022-9
Guven, 2012, A comparative study of predicting scour around a circular pile, Institution of Civil Engineers Journal Maritime Engineering, 165, 31, 10.1680/maen.2012.165.1.31
Guven, 2009, Linear genetic programming for prediction of circular pile scour, Journal of Oceanic Engineering, 36, 985, 10.1016/j.oceaneng.2009.05.010
Guven, 2013, Monthly pan evaporation modeling using linear genetic programming, Journal of Hydrology, 503, 178, 10.1016/j.jhydrol.2013.08.043
Haykin, 1998
Huang, 2014, Monthly streamflow prediction using modified EMD-based support vector machine, Journal of Hydrology, 511, 764, 10.1016/j.jhydrol.2014.01.062
Hull, 2008, Modelling dissolved oxygen dynamics in coastal lagoons, Ecological Modelling, 2, 468, 10.1016/j.ecolmodel.2007.09.023
Kalff, 2002
Karamouz, 2009, Probabilistic reservoir operation using Bayesian stochastic model and support vector machine, Advances in Water Resources, 32, 1588, 10.1016/j.advwatres.2009.08.003
Khan, 2006, Application of support vector machine in Lake water level prediction, Journal of Hydrologic Engineering, 11, 199, 10.1061/(ASCE)1084-0699(2006)11:3(199)
Kisi, 2013, Modeling of dissolved oxygen in river water using artificial intelligence techniques, Journal of Environmental Informatics, 22, 92, 10.3808/jei.201300248
Kisi, 2010, Evapotranspiration modeling using linear genetic programming technique, Journal of Irrigation and Drainage Engineering, 136, 715, 10.1061/(ASCE)IR.1943-4774.0000244
Koza, 1992
Lee, 1997, Genetic programming model for long term forecasting of electric power demand, Electric Power Systems Research, 40, 17, 10.1016/S0378-7796(96)01125-X
Lee, 2003, Radial basis function networks applied to DNBR calculation in digital core protection systems, Annals of Nuclear Energy, 30, 1516, 10.1016/S0306-4549(03)00099-9
Lin, 2006, Using support vector machines for long-term discharge prediction, Hydrological Sciences Journal, 51, 599, 10.1623/hysj.51.4.599
Liong, 2002, Flood stage forecasting with support vector machines, Journal of the American Water Resources Association, 38, 173, 10.1111/j.1752-1688.2002.tb01544.x
Lippman, 1987, An introduction to computing with neural nets, IEEE ASSP Magazine, 4, 4, 10.1109/MASSP.1987.1165576
Londhe, 2010, Comparison of data-driven modelling techniques for river flow forecasting, Hydrological Sciences Journal, 55, 1163, 10.1080/02626667.2010.512867
Luk, 2000, A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, Journal of Hydrology, 227, 56, 10.1016/S0022-1694(99)00165-1
Marti, 2013, Artificial neural networks vs. Gene Expression Programming for estimating outlet dissolved oxygen in micro-irrigations and filters fed with effluents, Computers and Electronics in Agriculture, 99, 176, 10.1016/j.compag.2013.08.016
Masters, 1993
Nourani, 2011, Two hybrid Artificial Intelligence approaches for modeling rainfall–runoff process, Journal of Hydrology, 402, 41, 10.1016/j.jhydrol.2011.03.002
Olyaie, 2015, Erratum to: A comparison of various artificial intelligence approaches performance for estimating suspended sediment load of river systems: a case study in United States, Environmental Monitoring and Assessment, 187, 187, 10.1007/s10661-015-4381-1
Partal, 2008, Estimation and forecasting of daily suspended sediment data using wavelet-neural networks, Journal of Hydrology, 358, 317, 10.1016/j.jhydrol.2008.06.013
Poli
Rankovic, 2010, Neural network modeling of dissolved oxygen in the Gruza reservoir, Serbia, Ecological Modelling, 221, 1239, 10.1016/j.ecolmodel.2009.12.023
Schmid, 2006, Artificial neural network modeling of dissolved oxygen in a wetland pond: the case of Hovi, Finland, Journal of Hydrologic Engineering, 11, 188, 10.1061/(ASCE)1084-0699(2006)11:2(188)
Shu, 2008, Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system, Journal of Hydrology, 349, 31, 10.1016/j.jhydrol.2007.10.050
Shukla, 2008, Mathematical modeling and analysis of the depletion of dissolved oxygen in eutrophied water bodies affected by organic pollutants, Nonlinear Analysis: Real World Applications, 9, 1851, 10.1016/j.nonrwa.2007.05.016
Singh, 2009, Artificial neural network modeling of the river water quality-A case study, Ecological Modelling, 220, 888, 10.1016/j.ecolmodel.2009.01.004
Sivapragasam, 2005, Discharge rating curve extension: a new approach, Water Resources Management, 19, 505, 10.1007/s11269-005-6811-2
Sreekanth, 2011, Coupled simulation-optimization model for coastal aquifer management using genetic programming-based ensemble surrogate models and multiple-realization optimization, Water Resources Management, 47, 1
Traore, 2013, New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa, Irrigation Science, 31, 1, 10.1007/s00271-011-0288-y
U.S. Geological Survey, 2015, National Water Information System data available on the World Wide Web (USGS Water Data for the Nation), accessed [Sep 10, 2015], at URL [http://waterdata.usgs.gov/nwis/].
Vapnik, 1995
Vapnik, 1998
Wankhede, 2005, Support vector machines for fingerprint classification, Proceedings of the Eleventh National Conference on Communications, 356
Wang, 2009, A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series, Journal of Hydrology, 374, 294, 10.1016/j.jhydrol.2009.06.019
Wu, 2008, River stage prediction based on a distributed support vector regression, Journal of Hydrology, 358, 96, 10.1016/j.jhydrol.2008.05.028
YSI., 2009, The dissolved Oxygen handbook, 76