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Volumn 32, Issue 6, 2018, Pages 1683-1697

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey

Author keywords

Firefly algorithm; Hybrid model; Lake Egirdir; MLP; Water level

Indexed keywords

BACKPROPAGATION; BIOLUMINESCENCE; FLOOD CONTROL; FORECASTING; LAKES; MEAN SQUARE ERROR; OPTIMIZATION;

EID: 85032193851     PISSN: 14363240     EISSN: 14363259     Source Type: Journal    
DOI: 10.1007/s00477-017-1474-0     Document Type: Article
Times cited : (106)

References (74)
  • 1
    • 84889646990 scopus 로고    scopus 로고
    • Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks
    • Abbot J, Marohasy J (2014) Input selection and optimisation for monthly rainfall forecasting in Queensland, Australia, using artificial neural networks. Atmos Res 138:166–178
    • (2014) Atmos Res , vol.138 , pp. 166-178
    • Abbot, J.1    Marohasy, J.2
  • 2
    • 77956838672 scopus 로고    scopus 로고
    • Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms
    • Adamowski J, Karapataki C (2010) Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J Hydrol Eng 15:729–743
    • (2010) J Hydrol Eng , vol.15 , pp. 729-743
    • Adamowski, J.1    Karapataki, C.2
  • 3
    • 84856246099 scopus 로고    scopus 로고
    • Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada
    • Adamowski J, Fung Chan H, Prasher SO, Ozga-Zielinski B, Sliusarieva A (2012) Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada. Water Resour Res 48(1). doi:10.1029/2010WR009945
    • (2012) Water Resour Res , vol.48 , Issue.1
    • Adamowski, J.1    Fung Chan, H.2    Prasher, S.O.3    Ozga-Zielinski, B.4    Sliusarieva, A.5
  • 4
    • 84925489228 scopus 로고    scopus 로고
    • ANN based sediment prediction model utilizing different input scenarios
    • Afan HA et al (2015) ANN based sediment prediction model utilizing different input scenarios. Water Resour Manag 29:1231–1245
    • (2015) Water Resour Manag , vol.29 , pp. 1231-1245
    • Afan, H.A.1
  • 5
    • 84870687035 scopus 로고    scopus 로고
    • Prediction of significant wave height using geno-multilayer perceptron
    • Altunkaynak A (2013) Prediction of significant wave height using geno-multilayer perceptron. Ocean Eng 58:144–153
    • (2013) Ocean Eng , vol.58 , pp. 144-153
    • Altunkaynak, A.1
  • 6
    • 35448986493 scopus 로고    scopus 로고
    • Fuzzy logic model of lake water level fluctuations in Lake Van, Turkey
    • Altunkaynak A, Şen Z (2007) Fuzzy logic model of lake water level fluctuations in Lake Van, Turkey. Theor Appl Climatol 90:227–233
    • (2007) Theor Appl Climatol , vol.90 , pp. 227-233
    • Altunkaynak, A.1    Şen, Z.2
  • 7
    • 0141750713 scopus 로고    scopus 로고
    • Triple diagram model of level fluctuations in Lake Van, Turkey
    • Altunkaynak A, Özger M, Sen Z (2003) Triple diagram model of level fluctuations in Lake Van, Turkey. Hydrol Earth Syst Sci Discuss 7:235–244
    • (2003) Hydrol Earth Syst Sci Discuss , vol.7 , pp. 235-244
    • Altunkaynak, A.1    Özger, M.2    Sen, Z.3
  • 8
    • 0027594152 scopus 로고
    • Criteria for evaluation of watershed models
    • ASCE (1993) Criteria for evaluation of watershed models. J Irrig Drain Eng 119:429–442
    • (1993) J Irrig Drain Eng , vol.119 , pp. 429-442
  • 9
    • 85027939411 scopus 로고    scopus 로고
    • Estimation of the change in lake water level by artificial intelligence methods
    • Buyukyildiz M, Tezel G, Yilmaz V (2014) Estimation of the change in lake water level by artificial intelligence methods. Water Resour Manag 28:4747–4763
    • (2014) Water Resour Manag , vol.28 , pp. 4747-4763
    • Buyukyildiz, M.1    Tezel, G.2    Yilmaz, V.3
  • 10
    • 84893812050 scopus 로고    scopus 로고
    • A support vector machine-firefly algorithm based forecasting model to determine malaria transmission
    • Ch S et al (2014) A support vector machine-firefly algorithm based forecasting model to determine malaria transmission. Neurocomputing 129:279–288
    • (2014) Neurocomputing , vol.129 , pp. 279-288
    • Ch, S.1
  • 11
    • 84903642315 scopus 로고    scopus 로고
    • Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature
    • Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247–1250
    • (2014) Geosci Model Dev , vol.7 , pp. 1247-1250
    • Chai, T.1    Draxler, R.R.2
  • 12
    • 0032579575 scopus 로고    scopus 로고
    • El Nino/Southern Oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting
    • Chiew FH, Piechota TC, Dracup JA, McMahon TA (1998) El Nino/Southern Oscillation and Australian rainfall, streamflow and drought: links and potential for forecasting. J Hydrol 204:138–149
    • (1998) J Hydrol , vol.204 , pp. 138-149
    • Chiew, F.H.1    Piechota, T.C.2    Dracup, J.A.3    McMahon, T.A.4
  • 13
    • 70350511636 scopus 로고    scopus 로고
    • Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey
    • Çimen M, Kisi O (2009) Comparison of two different data-driven techniques in modeling lake level fluctuations in Turkey. J Hydrol 378:253–262
    • (2009) J Hydrol , vol.378 , pp. 253-262
    • Çimen, M.1    Kisi, O.2
  • 14
    • 33846798345 scopus 로고    scopus 로고
    • HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts
    • Dawson CW, Abrahart RJ, See LM (2007) HydroTest: a web-based toolbox of evaluation metrics for the standardised assessment of hydrological forecasts. Environ Model Softw 22:1034–1052
    • (2007) Environ Model Softw , vol.22 , pp. 1034-1052
    • Dawson, C.W.1    Abrahart, R.J.2    See, L.M.3
  • 16
    • 84928136722 scopus 로고    scopus 로고
    • Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia
    • Deo RC, Şahin M (2015a) Application of the artificial neural network model for prediction of monthly standardized precipitation and evapotranspiration index using hydrometeorological parameters and climate indices in eastern Australia. Atmos Res 161–162:65–81
    • (2015) Atmos Res , vol.161-162 , pp. 65-81
    • Deo, R.C.1    Şahin, M.2
  • 17
    • 84908611342 scopus 로고    scopus 로고
    • Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia
    • Deo RC, Şahin M (2015b) Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia. Atmos Res 153:512–525
    • (2015) Atmos Res , vol.153 , pp. 512-525
    • Deo, R.C.1    Şahin, M.2
  • 18
    • 84954310363 scopus 로고    scopus 로고
    • An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland
    • Deo RC, Şahin M (2016) An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland. Environ Monit Assess. doi:10.1007/s10661-016-5094-9
    • (2016) Environ Monit Assess
    • Deo, R.C.1    Şahin, M.2
  • 19
    • 84994060685 scopus 로고    scopus 로고
    • Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model
    • Deo RC, Kisi O, Singh VP (2017a) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175
    • (2017) Atmos Res , vol.184 , pp. 149-175
    • Deo, R.C.1    Kisi, O.2    Singh, V.P.3
  • 20
    • 85030656566 scopus 로고    scopus 로고
    • Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data
    • (in press)
    • Deo RC et al (2017b) Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data. Renew Energy. doi:10.1016/j.renene.2017.09.078 (in press)
    • (2017) Renew Energy
    • Deo, R.C.1
  • 21
    • 84969872411 scopus 로고    scopus 로고
    • Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model
    • Deo RC, Tiwari MK, Adamowski JF, Quilty MJ (2017c) Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model. Stoch Environ Res Risk Assess 31(5):1211–1240
    • (2017) Stoch Environ Res Risk Assess , vol.31 , Issue.5 , pp. 1211-1240
    • Deo, R.C.1    Tiwari, M.K.2    Adamowski, J.F.3    Quilty, M.J.4
  • 23
    • 84957593712 scopus 로고    scopus 로고
    • Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review
    • Fahimi F, Yaseen ZM, El-shafie A (2017) Application of soft computing based hybrid models in hydrological variables modeling: a comprehensive review. Theor Appl Climatol 128:875–903
    • (2017) Theor Appl Climatol , vol.128 , pp. 875-903
    • Fahimi, F.1    Yaseen, Z.M.2    El-shafie, A.3
  • 24
    • 84934758341 scopus 로고    scopus 로고
    • Optimization of soil water characteristic curves parameters by modified firefly algorithm
    • Fu Q, Jiang R, Wang Z, Li T (2015) Optimization of soil water characteristic curves parameters by modified firefly algorithm. Trans Chin Soc Agric Eng 31:117–122
    • (2015) Trans Chin Soc Agric Eng , vol.31 , pp. 117-122
    • Fu, Q.1    Jiang, R.2    Wang, Z.3    Li, T.4
  • 25
    • 84880684699 scopus 로고    scopus 로고
    • Tree-based iterative input variable selection for hydrological modeling
    • Galelli S, Castelletti A (2013) Tree-based iterative input variable selection for hydrological modeling. Water Resour Res 49:4295–4310
    • (2013) Water Resour Res , vol.49 , pp. 4295-4310
    • Galelli, S.1    Castelletti, A.2
  • 26
    • 84982299870 scopus 로고    scopus 로고
    • Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains
    • Garousi-Nejad I, Bozorg-Haddad O, Loáiciga HA (2016) Modified firefly algorithm for solving multireservoir operation in continuous and discrete domains. J Water Resour Plan Manag 142:04016029
    • (2016) J Water Resour Plan Manag , vol.142 , pp. 04016029
    • Garousi-Nejad, I.1    Bozorg-Haddad, O.2    Loáiciga, H.A.3
  • 27
    • 84884589096 scopus 로고    scopus 로고
    • Relative importance of parameters affecting wind speed prediction using artificial neural networks
    • Ghorbani M, Khatibi R, Hosseini B, Bilgili M (2013) Relative importance of parameters affecting wind speed prediction using artificial neural networks. Theor Appl Climatol 114:107–114
    • (2013) Theor Appl Climatol , vol.114 , pp. 107-114
    • Ghorbani, M.1    Khatibi, R.2    Hosseini, B.3    Bilgili, M.4
  • 28
    • 85027831982 scopus 로고    scopus 로고
    • Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran
    • (in press)
    • Ghorbani MA, Deo RC, Yaseen ZMK, Mahasa H, Mohammad B (2017a) Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran. Theor Appl Climatol. doi:10.1007/s00704-017-2244-0 (in press)
    • (2017) Theor Appl Climatol
    • Ghorbani, M.A.1    Deo, R.C.2    Yaseen, Z.M.K.3    Mahasa, H.4    Mohammad, B.5
  • 29
    • 85019152630 scopus 로고    scopus 로고
    • Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point
    • Ghorbani MA et al (2017b) Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point. Soil Tillage Res 172:32–38
    • (2017) Soil Tillage Res , vol.172 , pp. 32-38
    • Ghorbani, M.A.1
  • 30
    • 78751576467 scopus 로고    scopus 로고
    • Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir Lake level forecasting
    • Güldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Eğirdir Lake level forecasting. Water Resour Manag 24:105–128
    • (2010) Water Resour Manag , vol.24 , pp. 105-128
    • Güldal, V.1    Tongal, H.2
  • 31
    • 84880605994 scopus 로고    scopus 로고
    • Approximation of modified Anderson-Darling test statistics for extreme value distributions with unknown shape parameter
    • Heo J-H, Shin H, Nam W, Jeong C (2013) Approximation of modified Anderson-Darling test statistics for extreme value distributions with unknown shape parameter. J Hydrol 499:41–49
    • (2013) J Hydrol , vol.499 , pp. 41-49
    • Heo, J.-H.1    Shin, H.2    Nam, W.3    Jeong, C.4
  • 32
    • 84880039776 scopus 로고    scopus 로고
    • Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS) Water
    • Hipni A et al (2013) Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS) Water. Resour Manag 27:3803–3823
    • (2013) Resour Manag , vol.27 , pp. 3803-3823
    • Hipni, A.1
  • 34
    • 84883792756 scopus 로고    scopus 로고
    • Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods
    • Kakahaji H, Banadaki HD, Kakahaji A, Kakahaji A (2013) Prediction of Urmia Lake water-level fluctuations by using analytical, linear statistic and intelligent methods. Water Resour Manag 27:4469–4492
    • (2013) Water Resour Manag , vol.27 , pp. 4469-4492
    • Kakahaji, H.1    Banadaki, H.D.2    Kakahaji, A.3    Kakahaji, A.4
  • 35
    • 84899800725 scopus 로고    scopus 로고
    • A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting
    • Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41:6047–6056
    • (2014) Expert Syst Appl , vol.41 , pp. 6047-6056
    • Kavousi-Fard, A.1    Samet, H.2    Marzbani, F.3
  • 36
    • 84911890700 scopus 로고    scopus 로고
    • ANN models optimized using swarm intelligence algorithms
    • Kayarvizhy N, Kanmani S, Uthariaraj R (2014) ANN models optimized using swarm intelligence algorithms. WSEAS Trans Comput 13:501–519
    • (2014) WSEAS Trans Comput , vol.13 , pp. 501-519
    • Kayarvizhy, N.1    Kanmani, S.2    Uthariaraj, R.3
  • 37
    • 84937764496 scopus 로고    scopus 로고
    • A modified firefly algorithm for engineering design optimization problems
    • Kazemzadeh-Parsi M (2014) A modified firefly algorithm for engineering design optimization problems. Iran J Sci Technol 38:403–421
    • (2014) Iran J Sci Technol , vol.38 , pp. 403-421
    • Kazemzadeh-Parsi, M.1
  • 38
    • 79956352538 scopus 로고    scopus 로고
    • Comparison of three artificial intelligence techniques for discharge routing
    • Khatibi R, Ghorbani MA, Kashani MH, Kisi O (2011) Comparison of three artificial intelligence techniques for discharge routing. J Hydrol 403:201–212
    • (2011) J Hydrol , vol.403 , pp. 201-212
    • Khatibi, R.1    Ghorbani, M.A.2    Kashani, M.H.3    Kisi, O.4
  • 39
    • 84897661948 scopus 로고    scopus 로고
    • Inter-comparison of time series models of lake levels predicted by several modeling strategies
    • Khatibi R et al (2014) Inter-comparison of time series models of lake levels predicted by several modeling strategies. J Hydrol 511:530–545
    • (2014) J Hydrol , vol.511 , pp. 530-545
    • Khatibi, R.1
  • 40
    • 61749102355 scopus 로고    scopus 로고
    • Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks
    • Kişi Ö (2009a) Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23:213–223
    • (2009) Hydrol Process , vol.23 , pp. 213-223
    • Kişi, Ö.1
  • 41
    • 67650333111 scopus 로고    scopus 로고
    • Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey
    • Kişi Ö (2009b) Neural network and wavelet conjunction model for modelling monthly level fluctuations in Turkey. Hydrol Process 23:2081–2092
    • (2009) Hydrol Process , vol.23 , pp. 2081-2092
    • Kişi, Ö.1
  • 42
    • 33748913014 scopus 로고    scopus 로고
    • Comparison of different efficiency criteria for hydrological model assessment
    • Krause P, Boyle D, Bäse F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97
    • (2005) Adv Geosci , vol.5 , pp. 89-97
    • Krause, P.1    Boyle, D.2    Bäse, F.3
  • 43
    • 84910651885 scopus 로고    scopus 로고
    • Mutual information-based multi-label feature selection using interaction information
    • Lee J, Kim D-W (2015) Mutual information-based multi-label feature selection using interaction information. Expert Syst Appl 42:2013–2025
    • (2015) Expert Syst Appl , vol.42 , pp. 2013-2025
    • Lee, J.1    Kim, D.-W.2
  • 44
    • 0032920124 scopus 로고    scopus 로고
    • Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation
    • Legates DR, McCabe GJ (1999) Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res 35:233–241
    • (1999) Water Resour Res , vol.35 , pp. 233-241
    • Legates, D.R.1    McCabe, G.J.2
  • 48
    • 51249194645 scopus 로고
    • A logical calculus of the ideas immanent in nervous activity
    • McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133
    • (1943) Bull Math Biophys , vol.5 , pp. 115-133
    • McCulloch, W.S.1    Pitts, W.2
  • 49
    • 79955881885 scopus 로고    scopus 로고
    • Drought modeling–a review
    • Mishra AK, Singh VP (2011) Drought modeling–a review. J Hydrol 403:157–175
    • (2011) J Hydrol , vol.403 , pp. 157-175
    • Mishra, A.K.1    Singh, V.P.2
  • 51
    • 0014776873 scopus 로고
    • River flow forecasting through conceptual models part I—a discussion of principles
    • Nash J, Sutcliffe J (1970) River flow forecasting through conceptual models part I—a discussion of principles. J Hydrol 10:282–290
    • (1970) J Hydrol , vol.10 , pp. 282-290
    • Nash, J.1    Sutcliffe, J.2
  • 52
    • 84925988131 scopus 로고    scopus 로고
    • A support vector machine–firefly algorithm-based model for global solar radiation prediction
    • Olatomiwa L et al (2015) A support vector machine–firefly algorithm-based model for global solar radiation prediction. Sol Energy 115:632–644
    • (2015) Sol Energy , vol.115 , pp. 632-644
    • Olatomiwa, L.1
  • 53
    • 85021292297 scopus 로고    scopus 로고
    • Input selection and performance optimization of ANN-based streamflow forecasting in a drought-prone Murray Darling Basin with IIS and MODWT
    • Prasad R, Deo RC, Li Y, Maraseni T (2017) Input selection and performance optimization of ANN-based streamflow forecasting in a drought-prone Murray Darling Basin with IIS and MODWT. Atmos Res 197:42–63
    • (2017) Atmos Res , vol.197 , pp. 42-63
    • Prasad, R.1    Deo, R.C.2    Li, Y.3    Maraseni, T.4
  • 54
    • 84979491168 scopus 로고    scopus 로고
    • Bootstrap rank-ordered conditional mutual information (broCMI)—a nonlinear input variable selection method for water resources modeling
    • Quilty J, Adamowski J, Khalil B, Rathinasamy M (2016) Bootstrap rank-ordered conditional mutual information (broCMI)—a nonlinear input variable selection method for water resources modeling. Water Resour Res. doi:10.1002/2015WR016959
    • (2016) Water Resour Res
    • Quilty, J.1    Adamowski, J.2    Khalil, B.3    Rathinasamy, M.4
  • 55
    • 85026261765 scopus 로고    scopus 로고
    • Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River
    • Raheli B, Aalami MT, El-Shafie A, Ghorbani MA, Deo RC (2017) Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River. Environ Earth Sci 76:503
    • (2017) Environ Earth Sci , vol.76 , pp. 503
    • Raheli, B.1    Aalami, M.T.2    El-Shafie, A.3    Ghorbani, M.A.4    Deo, R.C.5
  • 56
    • 79960810686 scopus 로고    scopus 로고
    • Hybrid particle swarm and neural network approach for streamflow forecasting
    • Sedki A, Ouazar D (2010) Hybrid particle swarm and neural network approach for streamflow forecasting. Math Model Nat Phenom 5:132–138
    • (2010) Math Model Nat Phenom , vol.5 , pp. 132-138
    • Sedki, A.1    Ouazar, D.2
  • 57
    • 84855825466 scopus 로고    scopus 로고
    • Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India
    • Singh A, Imtiyaz M, Isaac R, Denis D (2012) Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India. Agric Water Manag 104:113–120
    • (2012) Agric Water Manag , vol.104 , pp. 113-120
    • Singh, A.1    Imtiyaz, M.2    Isaac, R.3    Denis, D.4
  • 58
    • 84966632250 scopus 로고    scopus 로고
    • A novel method to water level prediction using RBF and FFA
    • Soleymani SA et al (2016) A novel method to water level prediction using RBF and FFA. Water Resour Manag 30:3265–3283
    • (2016) Water Resour Manag , vol.30 , pp. 3265-3283
    • Soleymani, S.A.1
  • 59
    • 77953692036 scopus 로고    scopus 로고
    • Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression
    • Tabari H, Marofi S, Sabziparvar A-A (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406
    • (2010) Irrig Sci , vol.28 , pp. 399-406
    • Tabari, H.1    Marofi, S.2    Sabziparvar, A.-A.3
  • 61
    • 0034962651 scopus 로고    scopus 로고
    • Summarizing multiple aspects of model performance in a single diagram
    • Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192
    • (2001) J Geophys Res Atmos , vol.106 , pp. 7183-7192
    • Taylor, K.E.1
  • 62
    • 84885070735 scopus 로고    scopus 로고
    • Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models
    • Tiwari MK, Adamowski J (2013) Urban water demand forecasting and uncertainty assessment using ensemble wavelet-bootstrap-neural network models. Water Resour Res 49:6486–6507
    • (2013) Water Resour Res , vol.49 , pp. 6486-6507
    • Tiwari, M.K.1    Adamowski, J.2
  • 63
    • 84982262059 scopus 로고    scopus 로고
    • Prediction of water level using monthly lagged data in Lake Urmia, Iran
    • Vaheddoost B, Aksoy H, Abghari H (2016) Prediction of water level using monthly lagged data in Lake Urmia, Iran. Water Resour Manag 30:4951–4967
    • (2016) Water Resour Manag , vol.30 , pp. 4951-4967
    • Vaheddoost, B.1    Aksoy, H.2    Abghari, H.3
  • 64
    • 39149141586 scopus 로고    scopus 로고
    • SSA, PCA, TDPSC, ACFA: useful combination of methods for analysis of short and nonstationary time series
    • Vitanov NK, Sakai K, Dimitrova ZI (2008) SSA, PCA, TDPSC, ACFA: useful combination of methods for analysis of short and nonstationary time series. Chaos Solitons Fractals 37:187–202
    • (2008) Chaos Solitons Fractals , vol.37 , pp. 187-202
    • Vitanov, N.K.1    Sakai, K.2    Dimitrova, Z.I.3
  • 65
    • 79951800404 scopus 로고    scopus 로고
    • A hybrid neural network model for cyanobacteria bloom in Dianchi Lake
    • Wang Z, Huang K, Zhou P, Guo H (2010) A hybrid neural network model for cyanobacteria bloom in Dianchi Lake. Procedia Environ Sci 2:67–75
    • (2010) Procedia Environ Sci , vol.2 , pp. 67-75
    • Wang, Z.1    Huang, K.2    Zhou, P.3    Guo, H.4
  • 66
    • 85037042999 scopus 로고    scopus 로고
    • Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin
    • Wen X, Feng Q, Deo RC, Wu M, Si J (2016) Wavelet analysis–artificial neural network conjunction models for multi-scale monthly groundwater level predicting in an arid inland river basin, northwestern China. Hydrol Res 48(5). doi:10.2166/nh.2016.2396
    • (2016) Northwestern China. Hydrol Res , vol.48 , Issue.5
    • Wen, X.1    Feng, Q.2    Deo, R.C.3    Wu, M.4    Si, J.5
  • 67
    • 0019707668 scopus 로고
    • On the validation of models
    • Willmott CJ (1981) On the validation of models. Phys Geogr 2:184–194
    • (1981) Phys Geogr , vol.2 , pp. 184-194
    • Willmott, C.J.1
  • 68
    • 0020386641 scopus 로고
    • Some comments on the evaluation of model performance
    • Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313
    • (1982) Bull Am Meteorol Soc , vol.63 , pp. 1309-1313
    • Willmott, C.J.1
  • 69
    • 79953855364 scopus 로고    scopus 로고
    • Firefly algorithm, stochastic test functions and design optimisation
    • Yang X-S (2010a) Firefly algorithm, stochastic test functions and design optimisation. Int J Bio-Inspired Comput 2:78–84
    • (2010) Int J Bio-Inspired Comput , vol.2 , pp. 78-84
    • Yang, X.-S.1
  • 71
    • 58849094959 scopus 로고    scopus 로고
    • Modelling level change in lakes using neuro-fuzzy and artificial neural networks
    • Yarar A, Onucyıldız M, Copty NK (2009) Modelling level change in lakes using neuro-fuzzy and artificial neural networks. J Hydrol 365:329–334
    • (2009) J Hydrol , vol.365 , pp. 329-334
    • Yarar, A.1    Onucyıldız, M.2    Copty, N.K.3
  • 72
    • 84994591754 scopus 로고    scopus 로고
    • Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq
    • Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq. J Hydrol 542:603–614
    • (2016) J Hydrol , vol.542 , pp. 603-614
    • Yaseen, Z.M.1    Jaafar, O.2    Deo, R.C.3    Kisi, O.4    Adamowski, J.5    Quilty, J.6    El-Shafie, A.7
  • 74
    • 77955735474 scopus 로고    scopus 로고
    • Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions
    • Zadeh MR, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24:2673–2688
    • (2010) Water Resour Manag , vol.24 , pp. 2673-2688
    • Zadeh, M.R.1    Amin, S.2    Khalili, D.3    Singh, V.P.4


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