Abstract
Since the middle of the twentieth century, artificial intelligence (AI) models have been used widely in engineering and science problems. Water resource variable modeling and prediction are the most challenging issues in water engineering. Artificial neural network (ANN) is a common approach used to tackle this problem by using viable and efficient models. Numerous ANN models have been successfully developed to achieve more accurate results. In the current review, different ANN models in water resource applications and hydrological variable predictions are reviewed and outlined. In addition, recent hybrid models and their structures, input preprocessing, and optimization techniques are discussed and the results are compared with similar previous studies. Moreover, to achieve a comprehensive view of the literature, many articles that applied ANN models together with other techniques are included. Consequently, coupling procedure, model evaluation, and performance comparison of hybrid models with conventional ANN models are assessed, as well as, taxonomy and hybrid ANN models structures. Finally, current challenges and recommendations for future researches are indicated and new hybrid approaches are proposed.
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Abbreviations
- AAE:
-
Average absolute error
- AARE:
-
Average absolute relative error
- ACCDIFF:
-
Accumulated difference
- AIC:
-
Akaike information criterion
- ANFIS:
-
Adaptive neuro-fuzzy inference system
- ANN:
-
Artificial neural network
- APE:
-
Average percentage error
- AR:
-
Autoregressive
- ARE:
-
Absolute relative error
- ARIMA:
-
Autoregressive integrated moving average
- ARIMAX:
-
Autoregressive integrated moving average with exogenous input
- ARMA:
-
Autoregressive moving average
- ARV:
-
Average relative variance
- ATNN:
-
Adaptive time-delay neural network
- ATp:
-
The error of the time for peak to arrive
- BANN:
-
Bootstrapped artificial neural network
- BIC:
-
Bayesian information criterion
- BP:
-
Back-propagation
- BPNN:
-
Back-propagation neural network
- C:
-
Coefficient
- CANN:
-
Cluster-based ANN
- CG:
-
Conjugate gradient
- CM:
-
Confusion matrix
- CNNs:
-
Computational neural networks
- d:
-
Index of agreement
- d1:
-
The adjusted index of agreement
- DBP:
-
Division-based BP
- E:
-
Nash-Sutcliffe efficiency
- E1:
-
Modified coefficient of efficiency
- EANN:
-
Evolutionary artificial neural network
- EKFQ:
-
Extended Kalman filtering
- ENN:
-
Ensemble neural networks
- EQp%:
-
The error of peak discharge
- Fc:
-
Fuzzy partition coefficient
- FCM:
-
Fuzzy c-means
- FE:
-
Forecasting error
- FFBP:
-
Feed-forward back-propagation
- FFNN:
-
Feed-forward neural networks
- GA:
-
Genetic algorithm
- GEP:
-
Gene expression programming
- GP:
-
Genetic programming
- GRNN:
-
Generalized regression neural networks
- IDNN:
-
Input delay neural network
- IGANFIS:
-
Integrated geomorphological adaptive neuro-fuzzy inference system
- IVF:
-
Index of volumetric fit
- JNN:
-
Jordan recurrent neural network
- KNNs/K-nn:
-
K-nearest neighbor
- LLR:
-
Local linear regression model
- LM:
-
Levenberg-Marquardet
- LMBP:
-
Levenberg-Marquardet back-propagation
- MA:
-
Moving average
- MAE:
-
Mean absolute error
- MA-ANN:
-
Moving average artificial neural networks
- MANN:
-
Modular artificial neural network
- MAPE:
-
Mean absolute percentage error
- MARE:
-
Mean absolute relative error
- MAXAE:
-
Maximum absolute error
- MBE:
-
Mean bias error
- ME:
-
Mean error
- MINAE:
-
Minimum absolute error
- MLP:
-
Multilayer perceptron
- MLPNN:
-
Multilayer perceptron neural network
- MRE:
-
Mean relative error
- MSE:
-
Mean squared error
- NormBIC:
-
Normalized Bayesian information criteria
- NMBE:
-
Normalized mean bias error
- NMSE:
-
Normalized mean squared error
- NRMSE:
-
Normalized root mean squared error
- NSC:
-
Nash-Sutcliffe coefficient
- PANN:
-
Periodic ANN
- PCA:
-
Principal component analysis
- PE:
-
Relative peak error
- PI:
-
Coefficient of persistence index
- r :
-
Pearson coefficient of correlation
- R :
-
Correlation coefficient
- R 2 :
-
Coefficient of determination
- RAEp(%):
-
Ratio of absolute error of peak flow
- RBF:
-
Radial basis function
- RBFNN:
-
Radial basis function neural network
- R-Bias:
-
Relative bias
- RE:
-
Maximum relative error
- RME:
-
Relative mean error
- RMSE:
-
Root mean squared error
- R-RMSE:
-
Relative root mean squared error
- RT:
-
Regression trees
- RTRL:
-
Real-time recurrent learning
- S :
-
Slope
- S2d:
-
Variance of the distribution of differences with MBE
- SARIMAX:
-
Seasonal autoregressive integrated moving average with exogenous input
- SE:
-
Standard error
- SEE:
-
Standard error of estimate
- %SEP:
-
Percent standard error of prediction
- SI:
-
Scatter index
- SOFNN:
-
Self-organizing fuzzy neural networks
- SOM:
-
Self-organizing map
- SONO:
-
Self-organizing nonlinear output map
- SORB:
-
Self-organizing radial basis
- SS:
-
Skill score
- SSA:
-
Singular spectrum analysis
- SSE:
-
Sum of square errors
- SSNN:
-
State space neural network
- SVM:
-
Support vector machine
- SWMM:
-
Storm water management model
- TANN:
-
Threshold-based ANN
- TDRNN:
-
Time-delay recurrent neural network
- TNN:
-
Tevere neural network
- TS:
-
Threshold statistic
- VER%:
-
The error of total discharge volume
- WNN:
-
Wavelet neural network
- WT:
-
Wavelet transform
References
Abdullah SS, Malek MA (2016) Empirical Penman-Monteith equation and artificial intelligence techniques in predicting reference evapotranspiration: a review. Int J Water 10:55–66. doi:10.1504/IJW.2016.073741
Abrahart RJ, See L (2000) Comparing neural network and autoregressive moving average techniques for the provision of continuous river flow forecasts in two contrasting catchments. Hydrol Process 14:2157–2172. doi:10.1002/1099-1085(20000815/30)14:11/12<2157::AID-HYP57>3.0.CO;2-S
Abrahart RJ, See LM, Dawson CW et al (2010) Nearly two decades of neural network hydrologic modeling. Adv Data-Based Approaches Hydrol Model Forecast NJ World Sci Publ 267–346
Abrahart RJ, Anctil F, Coulibaly P et al (2012) Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog Phys Geogr 36:480–513. doi:10.1177/0309133312444943
Abudu S, Cui C, King JP et al (2011) Modeling of daily pan evaporation using partial least squares regression. Sci China Technol Sci 54:163–174. doi:10.1007/s11431-010-4205-z
Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407:28–40. doi:10.1016/j.jhydrol.2011.06.013
Adamowski J, Sun K (2010) Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds. J Hydrol 390:85–91. doi:10.1016/j.jhydrol.2010.06.033
Adeloye A (2009) The relative utility of regression and artificial neural networks models for rapidly predicting the capacity of water supply reservoirs. Environ Model Softw 24:1233–1240. doi:10.1016/j.envsoft.2009.04.002
Adeloye AJ, De Munari A (2006) Artificial neural network based generalized storage-yield-reliability models using the Levenberg-Marquardt algorithm. J Hydrol 326:215–230. doi:10.1016/j.jhydrol.2005.10.033
Afan HA, El-Shafie A, Yaseen ZM et al (2014) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245. doi:10.1007/s11269-014-0870-1
Akiner ME, Akkoyunlu A (2012) Modeling and forecasting river flow rate from the Melen Watershed, Turkey. J Hydrol 456–457:121–129. doi:10.1016/j.jhydrol.2012.06.031
Almeida LM, Ludermir TB (2010) A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks. Neurocomputing 73:1438–1450
Alp M, Cigizoglu H (2007) Suspended sediment load simulation by two artificial neural network methods using hydrometeorological data. Environ Model Softw 22:2–13. doi:10.1016/j.envsoft.2005.09.009
Alvisi S, Franchini M (2011) Fuzzy neural networks for water level and discharge forecasting with uncertainty. Environ Model Softw 26:523–537. doi:10.1016/j.envsoft.2010.10.016
Amisigo BA, van de Giesen N, Rogers C et al (2008) Monthly streamflow prediction in the Volta Basin of West Africa: a SISO NARMAX polynomial modelling. Phys Chem Earth A B C 33:141–150. doi:10.1016/j.pce.2007.04.019
Anctil F, Perrin C, Andréassian V (2004) Impact of the length of observed records on the performance of ANN and of conceptual parsimonious rainfall-runoff forecasting models. Environ Model Softw 19:357–368. doi:10.1016/S1364-8152(03)00135-X
Antar MA, Elassiouti I, Allam MN (2006) Rainfall–runoff modelling using artificial neural networks technique: a Blue Nile catchment case study. Hydrol Process 20:1201–1216
Aqil M, Kita I, Yano A, Nishiyama S (2007) A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff. J Hydrol 337:22–34. doi:10.1016/j.jhydrol.2007.01.013
Araghinejad S, Azmi M, Kholghi M (2011) Application of artificial neural network ensembles in probabilistic hydrological forecasting. J Hydrol 407:94–104. doi:10.1016/j.jhydrol.2011.07.011
Asadi S, Shahrabi J, Abbaszadeh P, Tabanmehr S (2013) A new hybrid artificial neural networks for rainfall-runoff process modeling. Neurocomputing 121:470–480. doi:10.1016/j.neucom.2013.05.023
Awchi TA (2014) River discharges forecasting in Northern Iraq using different ANN techniques. Water Resour Manag 1–14. doi: 10.1007/s11269-014-0516-3
Balestrassi PP, Popova E, Paiva AP, Marangon Lima JW (2009) Design of experiments on neural network’s training for nonlinear time series forecasting. Neurocomputing 72:1160–1178. doi:10.1016/j.neucom.2008.02.002
Bayram A, Kankal M, Onsoy H (2012) Estimation of suspended sediment concentration from turbidity measurements using artificial neural networks. Environ Monit Assess 184:4355–4365. doi:10.1007/s10661-011-2269-2
Bayram A, Kankal M, Tayfur G, Önsoy H (2013) Prediction of suspended sediment concentration from water quality variables. Neural Comput Applic 24:1079–1087. doi:10.1007/s00521-012-1333-3
Bazartseren B, Hildebrandt G, Holz K-P (2003) Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55:439–450. doi:10.1016/S0925-2312(03)00388-6
Boné R, Crucianu M, Asselin de Beauville JP (2002) Learning long-term dependencies by the selective addition of time-delayed connections to recurrent neural networks. Neurocomputing 48:251–266. doi:10.1016/S0925-2312(01)00654-3
Bowden GJ, Dandy GC, Maier HR (2005) Input determination for neural network models in water resources applications. Part 1 - Background and methodology. J Hydrol 301:75–92. doi:10.1016/j.jhydrol.2004.06.021
Brownlie W, Brooks NH (1981) Compilation of alluvial channel data: laboratory and field. California Institute of Technology, WM Keck Laboratory of Hydraulics and Water Resources
Bruton JM, McClendon RW, Hoogenboom G (2000) Estimating daily pan evaporation with artificial neural networks. Trans ASAE 43:491–496. doi:10.13031/2013.2730
Cannon AJ, Whitfield PH (2002) Downscaling recent streamflow conditions in British Columbia, Canada using ensemble neural network models. J Hydrol 259:136–151. doi:10.1016/S0022-1694(01)00581-9
Carcano EC, Bartolini P, Muselli M, Piroddi L (2008) Jordan recurrent neural network versus IHACRES in modelling daily streamflows. J Hydrol 362:291–307. doi:10.1016/j.jhydrol.2008.08.026
Castellano-Méndez M, González-Manteiga W, Febrero-Bande M et al (2004) Modelling of the monthly and daily behaviour of the runoff of the Xallas river using Box–Jenkins and neural networks methods. J Hydrol 296:38–58. doi:10.1016/j.jhydrol.2004.03.011
Chang FJ, Chen YC (2001) A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction. J Hydrol 245:153–164. doi:10.1016/S0022-1694(01)00350-X
Chen J, Adams BJ (2006) Integration of artificial neural networks with conceptual models in rainfall-runoff modeling. J Hydrol 318:232–249. doi:10.1016/j.jhydrol.2005.06.017
Chen YH, Chang FJ (2009) Evolutionary artificial neural networks for hydrological systems forecasting. J Hydrol 367:125–137. doi:10.1016/j.jhydrol.2009.01.009
Cheng CT, Xie JX, Chau KW, Layeghifard M (2008) A new indirect multi-step-ahead prediction model for a long-term hydrologic prediction. J Hydrol 361:118–130. doi:10.1016/j.jhydrol.2008.07.040
Chiang YM, Chang LC, Chang FJ (2004) Comparison of static-feedforward and dynamic-feedback neural networks for rainfall-runoff modeling. J Hydrol 290:297–311. doi:10.1016/j.jhydrol.2003.12.033
Choi DJ, Park H (2001) A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process. Water Res 35:3959–3967. doi:10.1016/S0043-1354(01)00134-8
Chua LH, Holz K-P (2005) Hybrid neural network—finite element river flow model. J Hydraul Eng 131:52–59
Chua LHC, Wong TSW (2011) Runoff forecasting for an asphalt plane by Artificial Neural Networks and comparisons with kinematic wave and autoregressive moving average models. J Hydrol 397:191–201. doi:10.1016/j.jhydrol.2010.11.030
Cigizoglu HK, Alp M (2006) Generalized regression neural network in modelling river sediment yield. Adv Eng Softw 37:63–68. doi:10.1016/j.advengsoft.2005.05.002
Cobaner M, Unal B, Kisi O (2009) Suspended sediment concentration estimation by an adaptive neuro-fuzzy and neural network approaches using hydro-meteorological data. J Hydrol 367:52–61. doi:10.1016/j.jhydrol.2008.12.024
Corzo G, Solomatine D (2007) Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting. Neural Netw 20:528–536. doi:10.1016/j.neunet.2007.04.019
Coulibaly P, Anctil F, Bobee B (1999) Hydrological forecasting with artificial neural networks: The state of the art. Can J Civ Eng 26:293–304
Coulibaly P, Anctil F, Bobée B (2000) Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. J Hydrol 230:244–257. doi:10.1016/S0022-1694(00)00214-6
Dawson CW, Wilby RL (2001) Hydrological modelling using artificial neural networks. Prog Phys Geogr 25:80–108. doi:10.1177/030913330102500104
De Bruin HAR, Kohsiek W, Van Den Hurk BJJM (1993) A verification of some methods to determine the fluxes of momentum, sensible heat, and water vapour using standard deviation and structure parameter of scalar meteorological quantities. Bound-Lay Meteorol 63:231–257. doi:10.1007/BF00710461
de Villiers J, Barnard E (1993) Backpropagation neural nets with one and two hidden layers. IEEE Trans Neural Netw 4:136–141. doi:10.1109/72.182704
Díaz-Robles LA, Ortega JC, Fu JS et al (2008) A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile. Atmos Environ 42:8331–8340. doi:10.1016/j.atmosenv.2008.07.020
Dogan E, Gumrukcuoglu M, Sandalci M, Opan M (2010) Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems. Eng Appl Artif Intell 23:961–967. doi:10.1016/j.engappai.2010.03.007
Dorum A, Yarar A, Faik Sevimli M, Onüçyildiz M (2010) Modelling the rainfall–runoff data of susurluk basin. Expert Syst Appl 37:6587–6593. doi:10.1016/j.eswa.2010.02.127
El-Baroudy I, Elshorbagy A, Carey SK et al (2010) Comparison of three data-driven techniques in modelling the evapotranspiration process. J Hydroinform 12:365. doi:10.2166/hydro.2010.029
El-Shafie A, Noureldin A (2011) Generalized versus non-generalized neural network model for multi-lead inflow forecasting at Aswan High Dam. Hydrol Earth Syst Sci 15:841–858. doi:10.5194/hess-15-841-2011
El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556
El-Shafie A, Abdin AE, Noureldin A, Taha MR (2009) Enhancing inflow forecasting model at aswan high dam utilizing radial basis neural network and upstream monitoring stations measurements. Water Resour Manag 23:2289–2315. doi:10.1007/s11269-008-9382-1
El-Shafie A, Abdelazim T, Noureldin A (2010) Neural network modeling of time-dependent creep deformations in masonry structures. Neural Comput Applic 19:583–594
El-Shafie A, Noureldin A, Taha M et al (2012a) Dynamic versus static neural network model for rainfall forecasting at Klang River Basin, Malaysia. Hydrol Earth Syst Sci 16:1151–1169. doi:10.5194/hess-16-1151-2012
El-Shafie AH, El-Shafie A, Almukhtar A et al (2012b) Radial basis function neural networks for reliably forecasting rainfall. J Water Clim Chan 3:125–138
Elshorbagy A, Simonovic SP, Panu US (2002) Estimation of missing streamflow data using principles of chaos theory. J Hydrol 255:123–133
Eslamian SS, Gohari SA, Zareian MJ, Firoozfar A (2012) Estimating Penman--Monteith reference evapotranspiration using artificial neural networks and genetic algorithm: a case study. Arab J Sci Eng 37:935–944
Fallah-Mehdipour E, Bozorg Haddad O, Mariño MA (2013) Prediction and simulation of monthly groundwater levels by genetic programming. J Hydro Environment Res 7:253–260. doi:10.1016/j.jher.2013.03.005
Fayaed S, El-Shafie A, Jaafar O (2013) Integrated Artificial Neural Network (ANN) and Stochastic Dynamic Programming (SDP) Model for Optimal Release Policy. Water Resour Manag 27:3679–3696. doi:10.1007/s11269-013-0373-5
Fereydooni M, Rahnemaei M, Babazadeh H et al (2012) Comparison of artificial neural networks and stochastic models in river discharge forecasting, ( Case study : Ghara- Aghaj River, Fars Province, Iran). Afr J Agric Res 7:5446–5458. doi:10.5897/AJAR11.1091
Firat M, Güngör M (2010) Monthly total sediment forecasting using adaptive neuro fuzzy inference system. Stoch Env Res Risk A 24:259–270. doi:10.1007/s00477-009-0315-1
Foken T (2008) Micrometeorology. Springer-Verlag, Berlin
Furundzic D (1998) Application example of neural networks for time series analysis: rainfall–runoff modeling. Signal Process 64:383–396. doi:10.1016/S0165-1684(97)00203-X
Gao H, Zhang Z, Lai Y et al (2008) Continuous query scheduler based on operators clustering. J Cent South Univ Technol (Engl Ed) 15:830–834. doi:10.1007/s11771
Glezakos TJ, Tsiligiridis TA, Iliadis LS et al (2009) Feature extraction for time-series data: An artificial neural network evolutionary training model for the management of mountainous watersheds. Neurocomputing 73:49–59. doi:10.1016/j.neucom.2008.08.024
Goyal MK (2014) Modeling of Sediment Yield Prediction Using M5 Model Tree Algorithm and Wavelet Regression. Water Resour Manag 1991–2003. doi: 10.1007/s11269-014-0590-6
Graves D, Pedrycz W (2009) Fuzzy prediction architecture using recurrent neural networks. Neurocomputing 72:1668–1678. doi:10.1016/j.neucom.2008.07.009
Grossmann A, Morlet J (1984) Decomposition of Hardy Function into Square Integrable Wavelets of Constant Shape. SIAM J Math Anal 15:723–736. doi:10.1137/0515056
Hall MJ, Minns AW (1999) The classification of hydrologically homogeneous regions. Hydrol Sci J 44:693–704. doi:10.1080/02626669909492268
Hamed MM, Khalafallah MG, Hassanien EA (2004) Prediction of wastewater treatment plant performance using artificial neural networks. Environ Model Softw 19:919–928. doi:10.1016/j.envsoft.2003.10.005
Hamidi O, Poorolajal J, Sadeghifar M (2014) A comparative study of support vector machines and artificial neural networks for predicting precipitation in Iran. Theor Appl Climatol. doi:10.1007/s00704-014-1141-z
Han H-G, Qiao J-F (2013) A structure optimisation algorithm for feedforward neural network construction. Neurocomputing 99:347–357. doi:10.1016/j.neucom.2012.07.023
Hargreaves GH, Samani ZA (1985) Reference crop evapotranspiration from temperature. Appl Eng Agric 1:96–99. doi:10.13031/2013.26773
Hong Y-ST (2012) Dynamic nonlinear state-space model with a neural network via improved sequential learning algorithm for an online real-time hydrological modeling. J Hydrol 468–469:11–21. doi:10.1016/j.jhydrol.2012.08.001
Hong Y, Hsu K, Sorooshian S, Gao X (2005) Self-organizing nonlinear output (SONO): a neural network suitable for cloud patch–based rainfall estimation at small scales. Water Resour Res 41. doi:10.1029/2004WR003142
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366. doi:10.1016/0893-6080(89)90020-8
Hossain MS, El-shafie A (2013) Intelligent Systems in Optimizing Reservoir Operation Policy: A Review. Water Resour Manag 27:3387–3407. doi:10.1007/s11269-013-0353-9
Hosseinzadeh Talaee P (2014) Multilayer perceptron with different training algorithms for streamflow forecasting. Neural Comput Applic 24:695–703. doi:10.1007/s00521-012-1287-5
Hsu K, Gupta HV, Gao X et al (2002) Self-organizing linear output map (SOLO): An artificial neural network suitable for hydrologic modeling and analysis. Water Resour Res 38:38-1. doi:10.1029/2001WR000795
Hu TS, Lam KC, Ng ST (2005) A Modified Neural Network for Improving River Flow Prediction/Un Réseau de Neurones Modifié pour Améliorer la Prévision de L’écoulement Fluvial. Hydrolog Sci J 50:299–318. doi:10.1623/hysj.50.2.299.60649
Huo Z, Feng S, Kang S et al (2012) Integrated neural networks for monthly river flow estimation in arid inland basin of Northwest China. J Hydrol 420–421:159–170. doi:10.1016/j.jhydrol.2011.11.054
Iliadis LS, Maris F (2007) An Artificial Neural Network model for mountainous water-resources management: The case of Cyprus mountainous watersheds. Environ Model Softw 22:1066–1072. doi:10.1016/j.envsoft.2006.05.026
Isik S, Kalin L, Schoonover JE et al (2013) Modeling effects of changing land use/cover on daily streamflow: An Artificial Neural Network and curve number based hybrid approach. J Hydrol 485:103–112. doi:10.1016/j.jhydrol.2012.08.032
Jain A, Kumar AM (2007) Hybrid neural network models for hydrologic time series forecasting. Appl Soft Comput J 7:585–592. doi:10.1016/j.asoc.2006.03.002
Jain A, Srinivasulu S (2006) Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques. J Hydrol 317:291–306. doi:10.1016/j.jhydrol.2005.05.022
Jeong DI, Kim YO (2009) Combining single-value streamflow forecasts - A review and guidelines for selecting techniques. J Hydrol 377:284–299. doi:10.1016/j.jhydrol.2009.08.028
Jia Y, Culver TB (2006) Bootstrapped artificial neural networks for synthetic flow generation with a small data sample. J Hydrol 331:580–590. doi:10.1016/j.jhydrol.2006.06.005
Jothiprakash V, Magar RB (2012) Multi-time-step ahead daily and hourly intermittent reservoir inflow prediction by artificial intelligent techniques using lumped and distributed data. J Hydrol 450–451:293–307. doi:10.1016/j.jhydrol.2012.04.045
Ju Q, Yu Z, Hao Z et al (2009) Division-based rainfall-runoff simulations with BP neural networks and Xinanjiang model. Neurocomputing 72:2873–2883. doi:10.1016/j.neucom.2008.12.032
Kagoda PA, Ndiritu J, Ntuli C, Mwaka B (2010) Application of radial basis function neural networks to short-term streamflow forecasting. Phys Chem Earth 35:571–581. doi:10.1016/j.pce.2010.07.021
Kalteh AM, Berndtsson R (2007) Interpolating monthly precipitation by self-organizing map (SOM) and multilayer perceptron (MLP). Hydrol Sci J 52:305–317. doi:10.1623/hysj.52.2.305
Kalteh AM, Hjorth P, Berndtsson R (2008) Review of the self-organizing map (SOM) approach in water resources: Analysis, modelling and application. Environ Model Softw 23:835–845. doi:10.1016/j.envsoft.2007.10.001
Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural Networks for River Flow Prediction. J Comput Civ Eng 8:201–220. doi:10.1061/(ASCE)0887-3801(1994)8:2(201)
Karunasinghe DSK, Liong S-Y (2006) Chaotic time series prediction with a global model: artificial neural network. J Hydrol 323:92–105. doi:10.1016/j.jhydrol.2005.07.048
Kasiviswanathan KS, Cibin R, Sudheer KP, Chaubey I (2013) Constructing prediction interval for artificial neural network rainfall runoff models based on ensemble simulations. J Hydrol 499:275–288. doi:10.1016/j.jhydrol.2013.06.043
Kavzoglu T (2009) Increasing the accuracy of neural network classification using refined training data. Environ Model Softw 24:850–858. doi:10.1016/j.envsoft.2008.11.012
Kentel E (2009) Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates. J Hydrol 375:481–488. doi:10.1016/j.jhydrol.2009.06.051
Kerem Cigizoglu H, Kisi Ö̈ (2006) Methods to improve the neural network performance in suspended sediment estimation. J Hydrol 317:221–238. doi:10.1016/j.jhydrol.2005.05.019
Keskin ME, Terzi Ö (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11:65–70. doi:10.1061/(ASCE)1084-0699(2006)11:1(65)
Khajehzadeh M, El-Shafie A, Raihan T (2010) Modified particle swarm optimization for probabilistic slope stability analysis. Int J Phys Sci 5:2248–2258
Khajehzadeh M, Raihan Taha M, El-Shafie A, Eslami M (2012) Locating the general failure surface of earth slope using particle swarm optimisation. Civ Eng Environ Syst 29:41–57
Khalil M, Panu U, Lennox W (2001) Groups and neural networks based streamflow data infilling procedures. J Hydrol 241:153–176. doi:10.1016/S0022-1694(00)00332-2
Khashei M, Bijari M (2010) An artificial neural network (p, d, q) model for timeseries forecasting. Expert Syst Appl 37:479–489. doi:10.1016/j.eswa.2009.05.044
Khatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403:201–212. doi:10.1016/j.jhydrol.2011.03.007
Khoshhal J, Mokarram M (2012) Model for prediction of evapotranspiration using MLP neural network. Int J Environ Sci 3:1000–1009. doi:10.6088/ijes.2012030133008
Kim G, Barros AP (2001) Quantitative flood forecasting using multisensor data and neural networks. J Hydrol 246:45–62. doi:10.1016/j.jhydrol.2010.09.005
Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317. doi:10.1016/j.jhydrol.2007.12.014
Kim JW, Pachepsky YA (2010) Reconstructing missing daily precipitation data using regression trees and artificial neural networks for SWAT streamflow simulation. J Hydrol 394:305–314. doi:10.1016/j.jhydrol.2010.09.005
Kingston GB, Maier HR, Lambert MF (2005) Calibration and validation of neural networks to ensure physically plausible hydrological modeling. J Hydrol 314:158–176. doi:10.1016/j.jhydrol.2005.03.013
Kisi O (2005) Suspended sediment estimation using neuro-fuzzy and neural network approaches. Hydrol Sci J 50:683–696. doi:10.1623/hysj.2005.50.4.683
Kisi Ö (2006) Generalized regression neural networks for evapotranspiration modelling. Hydrol Sci J 51:1092–1105
Kisi O (2008) The potential of different ANN techniques in evapotranspiration modelling. Hydrol Process 22:2449–2460. doi:10.1002/hyp.6837
Kisi O (2010) Wavelet regression model for short-term streamflow forecasting. J Hydrol 389:344–353. doi:10.1016/j.jhydrol.2010.06.013
Kişi Ö (2010) River suspended sediment concentration modeling using a neural differential evolution approach. J Hydrol 389:227–235. doi:10.1016/j.jhydrol.2010.06.003
Kişi Ö (2013) Evolutionary neural networks for monthly pan evaporation modeling. J Hydrol 498:36–45. doi:10.1016/j.jhydrol.2013.06.011
Kisi O, Cigizoglu HK (2007) Comparison of different ANN techniques in river flow prediction. Civ Eng Environ Syst 24:211–231. doi:10.1080/10286600600888565
Kisi Ö, Ozturk O (2007) Adaptive neurofuzzy computing technique for evapotranspiration estimation. J Irrig Drain Eng 133:368–379
Kisi O, Yuksel I, Dogan E (2008) Modelling daily suspended sediment of rivers in Turkey using several data-driven techniques / Modélisation de la charge journalière en matières en suspension dans des rivières turques à l’aide de plusieurs techniques empiriques. Hydrol Sci J 53:1270–1285. doi:10.1623/hysj.53.6.1270
Kisi O, Shiri J, Nikoofar B (2012) Forecasting daily lake levels using artificial intelligence approaches. Comput Geosci 41:169–180. doi:10.1016/j.cageo.2011.08.027
Kisi O, Shiri J, Tombul M (2013) Modeling rainfall-runoff process using soft computing techniques. Comput Geosci 51:108–117. doi:10.1016/j.cageo.2012.07.001
Kralisch S, Fink M, Flügel W-A, Beckstein C (2003) A neural network approach for the optimisation of watershed management. Environ Model Softw 18:815–823. doi:10.1016/S1364-8152(03)00081-1
Kumar M, Raghuwanshi N, Singh R et al (2002) Estimating evapotranspiration using artificial neural network. J Irrig Drain Eng 128:224–233. doi:10.1061/(ASCE)0733-9437(2002)128:4(224)
Kuo CC, Gan TY, Yu PS (2010) Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan. J Hydrol 387:292–303. doi:10.1016/j.jhydrol.2010.04.020
Landeras G, Ortiz-Barredo A, López JJ (2008) Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country (Northern Spain). Agric Water Manag 95:553–565. doi:10.1016/j.agwat.2007.12.011
Leahy P, Kiely G, Corcoran G (2008) Structural optimisation and input selection of an artificial neural network for river level prediction. J Hydrol 355:192–201. doi:10.1016/j.jhydrol.2008.03.017
Lin GF, Chen LH (2006) Identification of homogeneous regions for regional frequency analysis using the self-organizing map. J Hydrol 324:1–9. doi:10.1016/j.jhydrol.2005.09.009
Lin G-F, Wu M-C (2009) A hybrid neural network model for typhoon-rainfall forecasting. J Hydrol 375:450–458. doi:10.1016/j.jhydrol.2009.06.047
Lin B, Syed M, Falconer RA (2008) Predicting faecal indicator levels in estuarine receiving waters—an integrated hydrodynamic and ANN modelling approach. Environ Model Softw 23:729–740. doi:10.1016/j.envsoft.2007.09.009
Linares-rodriguez A, Lara-fanego V, Pozo-vazquez D (2014) One-Day-Ahead Streamflow Forecasting Using Artificial Neural Networks and a Meteorological Mesoscale Model. doi: 10.1061/(ASCE)HE.1943-5584.0001163
Liu QJ, Shi ZH, Fang NF et al (2013) Modeling the daily suspended sediment concentration in a hyperconcentrated river on the Loess Plateau, China, using the Wavelet-ANN approach. Geomorphology 186:181–190. doi:10.1016/j.geomorph.2013.01.012
Luccarini L, Bragadin GL, Colombini G et al (2010) Formal verification of wastewater treatment processes using events detected from continuous signals by means of artificial neural networks. Case study: SBR plant. Environ Model Softw 25:648–660. doi:10.1016/j.envsoft.2009.05.013
Maheswaran R, Khosa R (2012a) Wavelet-Volterra coupled model for monthly stream flow forecasting. J Hydrol 450–451:320–335. doi:10.1016/j.jhydrol.2012.04.017
Maheswaran R, Khosa R (2012b) Comparative study of different wavelets for hydrologic forecasting. Comput Geosci 46:284–295. doi:10.1016/j.cageo.2011.12.015
Maier HR, Dandy GC (1998) Understanding the behaviour and optimising the performance of back-propagation neural networks: An empirical study. Environ Model Softw 13:179–191. doi:10.1016/S1364-8152(98)00019-X
Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw 15:101–124. doi:10.1016/S1364-8152(99)00007-9
Maier HR, Dandy GC (2001) Neural network based modelling of environmental variables: a systematic approach. Math Comput Model 33:669–682. doi:10.1016/S0895-7177(00)00271-5
Maier HR, Morgan N, Chow CWK (2004) Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters. Environ Model Softw 19:485–494. doi:10.1016/S1364-8152(03)00163-4
Maier HR, Jain A, Dandy GC, Sudheer KP (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ Model Softw 25:891–909. doi:10.1016/j.envsoft.2010.02.003
May DB, Sivakumar M (2009) Prediction of urban stormwater quality using artificial neural networks. Environ Model Softw 24:296–302. doi:10.1016/j.envsoft.2008.07.004
May RJ, Dandy GC, Maier HR, Nixon JB (2008a) Application of partial mutual information variable selection to ANN forecasting of water quality in water distribution systems. Environ Model Softw 23:1289–1299. doi:10.1016/j.envsoft.2008.03.008
May RJ, Maier HR, Dandy GC, Fernando TMKG (2008b) Non-linear variable selection for artificial neural networks using partial mutual information. Environ Model Softw 23:1312–1326. doi:10.1016/j.envsoft.2008.03.007
McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133. doi:10.1007/BF02478259
Minns AW, Hall MJ (1996) Artificial neural networks as rainfall- runoff models. Hydrol Sci J 41:399–418. doi:10.1080/02626669609491511
Moghaddamnia A, Ghafari Gousheh M, Piri J et al (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97. doi:10.1016/j.advwatres.2008.10.005
Moradkhani H, Hsu K, Gupta HV, Sorooshian S (2004) Improved streamflow forecasting using self-organizing radial basis function artificial neural networks. J Hydrol 295:246–262. doi:10.1016/j.jhydrol.2004.03.027
Murao H, Nishikawa I, Kitamura S (1993) A Hybrid Neural Network System for the Rainfall Estimation using Satellite Imagery. In: Proc. IJCNN-93, International Joint Conference on Neural Networks, Nagoya. pp 1211–1214
Mustafa MR, Rezaur RB, Saiedi S, Isa MH (2012) River suspended sediment prediction using various multilayer perceptron neural network training algorithm—a case study in Malaysia. Water Resour Manag 26:1879–1897. doi:10.1007/s11269-012-9992-5
Mutlu E, Chaubey I, Hexmoor H, Bajwa SG (2008) Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed. Hydrol Process 22:5097–5106. doi:10.1002/hyp.7136
Najah A, Elshafie A, Karim OA, Jaffar O (2009) Prediction of Johor River water quality parameters using artificial neural networks. Eur J Sci Res 28:422–435
Najah A, El-Shafie A, Karim OA, Jaafar O (2011) Integrated versus isolated scenario for prediction dissolved oxygen at progression of water quality monitoring stations. Hydrol Earth Syst Sci 15:2693–2708. doi:10.5194/hess-15-2693-2011
Napolitano G, See L, Calvo B et al (2010) A conceptual and neural network model for real-time flood forecasting of the Tiber River in Rome. Phys Chem Earth A B C 35:187–194. doi:10.1016/j.pce.2009.12.004
Nayak PC, Venkatesh B, Krishna B, Jain SK (2013) Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach. J Hydrol 493:57–67. doi:10.1016/j.jhydrol.2013.04.016
Nilsson P, Uvo CB, Berndtsson R (2006) Monthly runoff simulation: comparing and combining conceptual and neural network models. J Hydrol 321:344–363. doi:10.1016/j.jhydrol.2005.08.007
Nourani V, Komasi M (2013) A geomorphology-based ANFIS model for multi-station modeling of rainfall-runoff process. J Hydrol 490:41–55. doi:10.1016/j.jhydrol.2013.03.024
Nourani V, Kisi Ö, Komasi M (2011) Two hybrid Artificial Intelligence approaches for modeling rainfall-runoff process. J Hydrol 402:41–59. doi:10.1016/j.jhydrol.2011.03.002
Nourani V, Baghanam AH, Adamowski J, Gebremichael M (2013) Using self-organizing maps and wavelet transforms for space-time pre-processing of satellite precipitation and runoff data in neural network based rainfall-runoff modeling. J Hydrol 476:228–243. doi:10.1016/j.jhydrol.2012.10.054
Nourani V, Hosseini Baghanam A, Adamowski J, Kisi O (2014) Applications of hybrid wavelet–Artificial Intelligence models in hydrology: a review. J Hydrol 514:358–377. doi:10.1016/j.jhydrol.2014.03.057
Oehler F, Coco G, Green MO, Bryan KR (2012) A data-driven approach to predict suspended-sediment reference concentration under non-breaking waves. Cont Shelf Res 46:96–106. doi:10.1016/j.csr.2011.01.015
Oh SK, Roh SB, Pedrycz W, Ahn TC (2007) IG-based genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons. Neurocomputing 70:2783–2798. doi:10.1016/j.neucom.2006.10.151
Ömer Faruk D (2010) A hybrid neural network and ARIMA model for water quality time series prediction. Eng Appl Artif Intell 23:586–594. doi:10.1016/j.engappai.2009.09.015
Onkal-Engin G, Demir I, Engin SN (2005) Determination of the relationship between sewage odour and BOD by neural networks. Environ Model Softw 20:843–850. doi:10.1016/j.envsoft.2004.04.012
Pan T, Wang R (2004) State space neural networks for short term rainfall-runoff forecasting. J Hydrol 297:34–50. doi:10.1016/j.jhydrol.2004.04.010
Partal T, Cigizoglu HK (2008) Estimation and forecasting of daily suspended sediment data using wavelet-neural networks. J Hydrol 358:317–331. doi:10.1016/j.jhydrol.2008.06.013
Pektaş AO, Kerem Cigizoglu H (2013) ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient. J Hydrol 500:21–36. doi:10.1016/j.jhydrol.2013.07.020
Penman HL (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc London Ser A Math Phys Sci 193:120–145. doi:10.1098/rspa.1948.0037
Piotrowski AP, Napiorkowski JJ (2011) Optimizing neural networks for river flow forecasting - Evolutionary Computation methods versus the Levenberg-Marquardt approach. J Hydrol 407:12–27. doi:10.1016/j.jhydrol.2011.06.019
Piotrowski AP, Napiorkowski JJ (2013) A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling. J Hydrol 476:97–111. doi:10.1016/j.jhydrol.2012.10.019
Pulido-Calvo I, Gutiérrez-Estrada JC (2009) Improved irrigation water demand forecasting using a soft-computing hybrid model. Biosyst Eng 102:202–218. doi:10.1016/j.biosystemseng.2008.09.032
Pulido-Calvo I, Portela MM (2007) Application of neural approaches to one-step daily flow forecasting in Portuguese watersheds. J Hydrol 332:1–15. doi:10.1016/j.jhydrol.2006.06.015
Qiao J, Wang H (2008) A self-organizing fuzzy neural network and its applications to function approximation and forecast modeling. Neurocomputing 71:564–569. doi:10.1016/j.neucom.2007.07.026
Raghuwanshi NS, Singh R, Reddy LS (2006) Runoff and Sediment Yield Modeling Using Artificial Neural Networks: Upper Siwane River, India. J Hydrol Eng 11:71–79. doi:10.1061/(ASCE)1084-0699(2006)11:1(71)
Rahimikhoob A (2009) Estimating daily pan evaporation using artificial neural network in a semi-arid environment. Theor Appl Climatol 98:101–105. doi:10.1007/s00704-008-0096-3
Rai RK, Mathur BS (2008) Event-based sediment yield modeling using artificial neural network. Water Resour Manag 22:423–441. doi:10.1007/s11269-007-9170-3
Rajaee T (2011) Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers. Sci Total Environ 409:2917–2928. doi:10.1016/j.scitotenv.2010.11.028
Rajurkar MP, Kothyari UC, Chaube UC (2004) Modeling of the daily rainfall-runoff relationship with artificial neural network. J Hydrol 285:96–113. doi:10.1016/j.jhydrol.2003.08.011
Rampone S (2013) Three-and-six-month-before forecast of water resources in a karst aquifer in the Terminio massif (Southern Italy). Appl Soft Comput 13:4077–4086. doi:10.1016/j.asoc.2013.05.016
Rana G, Katerji N (2000) Measurement and estimation of actual evapotranspiration in the field under Mediterranean climate: A review. Eur J Agron 13:125–153. doi:10.1016/S1161-0301(00)00070-8
Remesan R, Shamim MA, Han D, Mathew J (2009) Runoff prediction using an integrated hybrid modelling scheme. J Hydrol 372:48–60. doi:10.1016/j.jhydrol.2009.03.034
Rossi F, Delannay N, Conan-Guez B, Verleysen M (2005) Representation of functional data in neural networks. Neurocomputing 64:183–210. doi:10.1016/j.neucom.2004.11.012
Sang Y-F (2013) A review on the applications of wavelet transform in hydrology time series analysis. Atmos Res 122:8–15. doi:10.1016/j.atmosres.2012.11.003
Sanikhani H, Kisi O, Nikpour MR, Dinpashoh Y (2012) Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques. Water Resour Manag 26:4347–4365. doi:10.1007/s11269-012-0148-4
See L, Openshaw S (2000) A hybrid multi-model approach to river level forecasting. Hydrol Sci J 45:523–536. doi:10.1080/02626660009492354
Sharma SK, Tiwari KN (2009) Bootstrap based artificial neural network (BANN) analysis for hierarchical prediction of monthly runoff in Upper Damodar Valley Catchment. J Hydrol 374:209–222. doi:10.1016/j.jhydrol.2009.06.003
Shiri J, Kisi O (2010) Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model. J Hydrol 394:486–493. doi:10.1016/j.jhydrol.2010.10.008
Sivakumar B, Wallender WW (2005) Predictability of river flow and suspended sediment transport in the Mississippi River basin: A non-linear deterministic approach. Earth Surf Process Landf 30:665–677. doi:10.1002/esp.1167
Sivakumar B, Jayawardena AW, Fernando TMKG (2002) River flow forecasting: Use of phase-space reconstruction and artificial neural networks approaches. J Hydrol 265:225–245. doi:10.1016/S0022-1694(02)00112-9
Sivapragasam C, Vanitha S, Muttil N et al (2013) Monthly flow forecast for Mississippi River basin using artificial neural networks. Neural Comput Applic. doi:10.1007/s00521-013-1419-6
Smith J, Eli RN (1995) Neural-network models of rainfall-runoff process. J Water Resour Plan Manag 121:499–508
Srikanthan R, McMahon TA (2001) Stochastic generation of annual, monthly and daily climate data: A review. Hydrol Earth Syst Sci 5:653–670. doi:10.5194/hess-5-653-2001
Sudheer K, Gosain A, Mohana Rangan D, Saheb S (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202
Sulaiman M, El-Shafie A, Karim O, Basri H (2011) Improved water level forecasting performance by using optimal steepness coefficients in an artificial neural network. Water Resour Manag 25:2525–2541
Talei A, Chua LHC (2012) Influence of lag time on event-based rainfall-runoff modeling using the data driven approach. J Hydrol 438–439:223–233. doi:10.1016/j.jhydrol.2012.03.027
Talei A, Chua LHC, Quek C, Jansson PE (2013) Runoff forecasting using a Takagi-Sugeno neuro-fuzzy model with online learning. J Hydrol 488:17–32. doi:10.1016/j.jhydrol.2013.02.022
Tayfur G, Karimi Y, Singh VP (2013) Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction. Water Resour Manag 27:2541–2554. doi:10.1007/s11269-013-0302-7
Terzi Ö, Önal S (2012) Application of artificial neural networks and multiple linear regression to forecast monthly river flow in Turkey. Afr J Agric Res 7:1317–1323. doi:10.5897/AJAR11.1426
Thornthwaite CW (1948) An Approach Toward a Rational Classification of Climate. Soil Sci 66:77
Tiwari MK, Chatterjee C (2010) Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach. J Hydrol 394:458–470. doi:10.1016/j.jhydrol.2010.10.001
Toro CHF, Gómez Meire S, Gálvez JF, Fdez-Riverola F (2013) A hybrid artificial intelligence model for river flow forecasting. Appl Soft Comput 13:3449–3458. doi:10.1016/j.asoc.2013.04.014
Toth E, Brath A (2007) Multistep ahead streamflow forecasting: Role of calibration data in conceptual and neural network modeling. Water Resour Res 43. doi: 10.1029/2006WR005383
Trajkovic S (2005) Temperature-Based Approaches for Estimating Reference Evapotranspiration. J Irrig Drain Eng 131:316–323. doi:10.1061/(ASCE)0733-9437(2005)131:4(316)
Traore S, Wang Y-M, Kerh T (2010) Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manag 97:707–714. doi:10.1016/j.agwat.2010.01.002
Vafakhah M (2012) Application of artificial neural networks and adaptive neuro-fuzzy inference system models to short-term streamflow forecasting. Can J Civ Eng 39:402–414. doi:10.1139/l2012-011
Valipour M, Banihabib ME, Behbahani SMR (2013) Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir. J Hydrol 476:433–441. doi:10.1016/j.jhydrol.2012.11.017
Valizadeh N, El-Shafie A (2013) Forecasting the level of reservoirs using multiple input fuzzification in ANFIS. Water Resour Manag 27:3319–3331
Wang W, Van Gelder PHAJM, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324:383–399. doi:10.1016/j.jhydrol.2005.09.032
Wang WC, Chau KW, Cheng CT, Qiu L (2009) A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. J Hydrol 374:294–306. doi:10.1016/j.jhydrol.2009.06.019
Wang Z, Huang K, Zhou PJ, Guo HC (2010) A hybrid neural network model for cyanobacteria bloom in Dianchi Lake. Procedia Environ Sci 2:67–75. doi:10.1016/j.proenv.2010.10.010
Wang Y, Wang H, Lei X et al (2011) Flood simulation using parallel genetic algorithm integrated wavelet neural networks. Neurocomputing 74:2734–2744. doi:10.1016/j.neucom.2011.03.018
Wei S, Yang H, Song J et al (2013) A wavelet-neural network hybrid modelling approach for estimating and predicting river monthly flows. Hydrol Sci J 58:374–389. doi:10.1080/02626667.2012.754102
Wieland R, Mirschel W (2008) Adaptive fuzzy modeling versus artificial neural networks. Environ Model Softw 23:215–224. doi:10.1016/j.envsoft.2007.06.004
Wieland R, Mirschel W, Zbell B et al (2010) A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling. Environ Model Softw 25:412–420. doi:10.1016/j.envsoft.2009.11.006
Wright JL (1988) Daily and Seasonal Evapotranspiration and Yield of Irrigated Alfalfa in Southern Idaho. Agron J 80:662. doi:10.2134/agronj1988.00021962008000040022x
Wu CL, Chau KW (2010) Data-driven models for monthly streamflow time series prediction. Eng Appl Artif Intell 23:1350–1367. doi:10.1016/j.engappai.2010.04.003
Wu CL, Chau KW (2011) Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J Hydrol 399:394–409. doi:10.1016/j.jhydrol.2011.01.017
Wu CL, Chau KW (2013) Prediction of rainfall time series using modular soft computing methods. Eng Appl Artif Intell 26:997–1007. doi:10.1016/j.engappai.2012.05.023
Wu JS, Han J, Annambhotla S, Bryant S (2005) Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows. J Hydrol Eng 10:216–222. doi:10.1061/(ASCE)1084-0699(2005)10:3(216)
Wu CL, Chau KW, Li YS (2009a) Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques. Water Resour Res 45:1–23. doi:10.1029/2007WR006737
Wu CL, Chau KW, Li YS (2009b) Methods to improve neural network performance in daily flows prediction. J Hydrol 372:80–93. doi:10.1016/j.jhydrol.2009.03.038
Wu CL, Chau KW, Fan C (2010) Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J Hydrol 389:146–167. doi:10.1016/j.jhydrol.2010.05.040
Yang Q, Shao J, Scholz M et al (2012) Multi-label classification models for sustainable flood retention basins. Environ Model Softw 32:27–36. doi:10.1016/j.envsoft.2012.01.001
Yaseen ZM, El-Shafie A, Afan HA et al (2015a) RBFNN versus FFNN for daily river flow forecasting at Johor River, Malaysia. Neural Comput Applic. doi:10.1007/s00521-015-1952-6
Yaseen ZM, El-shafie A, Jaafar O et al (2015b) Artificial intelligence based models for stream-flow forecasting: 2000–2015. J Hydrol 530:829–844. doi:10.1016/j.jhydrol.2015.10.038
Yilmaz AG, Imteaz MA, Jenkins G (2011) Catchment flow estimation using Artificial Neural Networks in the mountainous Euphrates Basin. J Hydrol 410:134–140. doi:10.1016/j.jhydrol.2011.09.031
Yonaba H, Anctil F, Fortin V (2010) Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Streamflow Forecasting. J Hydrol Eng 15:275–283. doi:10.1061/(ASCE)HE.1943-5584.0000188
Yoon H, Jun S-C, Hyun Y et al (2011) A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer. J Hydrol 396:128–138. doi:10.1016/j.jhydrol.2010.11.002
Yu W (2006) Multiple recurrent neural networks for stable adaptive control. Neurocomputing 70:430–444. doi:10.1016/j.neucom.2005.12.122
Zanetti SS, Sousa EF, Oliveira VP et al (2007) Estimating Evapotranspiration Using Artificial Neural Network and Minimum Climatological Data. J Irrig Drain Eng 133:83–89. doi:10.1061/(ASCE)0733-9437(2007)133:2(83)
Zealand CM, Burn DH, Simonovic SP (1999) Short term streamflow forecasting using artificial neural networks. J Hydrol 214:32–48. doi:10.1016/S0022-1694(98)00242-X
Zeng X, Kiviat KL, Sakaguchi K, Mahmoud AMA (2012) A toy model for monthly river flow forecasting. J Hydrol 452–453:226–231. doi:10.1016/j.jhydrol.2012.05.053
Zhang B, Govindaraju RS (2003) Geomorphology-based artificial neural networks (GANNs) for estimation of direct runoff over watersheds. J Hydrol 273:18–34. doi:10.1016/S0022-1694(02)00313-X
Zhang X, Liang F, Yu B, Zong Z (2011) Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting. J Hydrol 409:696–709. doi:10.1016/j.jhydrol.2011.09.002
Zounemat-Kermani M, Kisi O, Rajaee T (2013) Performance of radial basis and LM-feed forward artificial neural networks for predicting daily watershed runoff. Appl Soft Comput J 13:4633–4644. doi:10.1016/j.asoc.2013.07.007
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Fahimi, F., Yaseen, Z.M. & El-shafie, A. Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol 128, 875–903 (2017). https://doi.org/10.1007/s00704-016-1735-8
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DOI: https://doi.org/10.1007/s00704-016-1735-8