메뉴 건너뛰기




Volumn 61, Issue 12, 2016, Pages 2164-2178

Pre-processing of data-driven river flow forecasting models by singular value decomposition (SVD) technique

Author keywords

data driven models; large scale atmospheric circulation; pre processing; river flow forecasting; singular value decomposition (SVD)

Indexed keywords

ATMOSPHERIC MOVEMENTS; DATA HANDLING; FORECASTING; PRINCIPAL COMPONENT ANALYSIS; STREAM FLOW; WATER RESOURCES;

EID: 84976417264     PISSN: 02626667     EISSN: 21503435     Source Type: Journal    
DOI: 10.1080/02626667.2015.1085991     Document Type: Article
Times cited : (29)

References (59)
  • 1
    • 84859105089 scopus 로고    scopus 로고
    • Using Pacific Ocean climatic variability to improve hydrologic reconstructions
    • S.R.Anderson, et al., 2012. Using Pacific Ocean climatic variability to improve hydrologic reconstructions. Journal of Hydrology, 434–435, 69–77.
    • (2012) Journal of Hydrology , vol.434-435 , pp. 69-77
    • Anderson, S.R.1
  • 3
    • 84979976947 scopus 로고    scopus 로고
    • A review of climate signals as predictors of long-term hydro-climatic variability
    • Tarhule A., (ed), InTech
    • S.Araghinejad, and E.Meidani, 2014. A review of climate signals as predictors of long-term hydro-climatic variability. In:A.Tarhule, ed. Climate variability - regional and thematic patterns. Croatia:InTech, 107–131.
    • (2014) Climate variability - regional and thematic patterns , pp. 107-131
    • Araghinejad, S.1    Meidani, E.2
  • 4
    • 84921903784 scopus 로고    scopus 로고
    • Adaptive policy responses to water shortage mitigation in the arid regions—a systematic approach based on eDPSIR, DEMATEL, and MCDA
    • A.Azarnivand, and N.Chitsaz, 2015. Adaptive policy responses to water shortage mitigation in the arid regions—a systematic approach based on eDPSIR, DEMATEL, and MCDA. Environmental Monitoring and Assessment, 187 (2), 1–15. doi:10.1007/s10661-014-4225-4
    • (2015) Environmental Monitoring and Assessment , vol.187 , Issue.2 , pp. 1-15
    • Azarnivand, A.1    Chitsaz, N.2
  • 5
    • 77955216778 scopus 로고    scopus 로고
    • Identification of Pacific Ocean sea surface temperature influences of Upper Colorado River Basin snowpack
    • O.A.Aziz, et al., 2010. Identification of Pacific Ocean sea surface temperature influences of Upper Colorado River Basin snowpack. Water Resources Research, 46, 7. doi:10.1029/2009WR008053
    • (2010) Water Resources Research , vol.46 , pp. 7
    • Aziz, O.A.1
  • 6
    • 84870242425 scopus 로고    scopus 로고
    • Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks
    • N.Ceryan, U.Okkan, and A.Kesimal, 2012. Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks. Rock Mechanics and Rock Engineering, 45 (6), 1055–1072. doi:10.1007/s00603-012-0239-9
    • (2012) Rock Mechanics and Rock Engineering , vol.45 , Issue.6 , pp. 1055-1072
    • Ceryan, N.1    Okkan, U.2    Kesimal, A.3
  • 7
    • 69849094925 scopus 로고    scopus 로고
    • Variabilities of the spring river runoff system in East China and their relations to precipitation and sea surface temperature
    • W.Chen, et al., 2009. Variabilities of the spring river runoff system in East China and their relations to precipitation and sea surface temperature. International Journal of Climatology, 29, 1381–1394. doi:10.1002/joc.v29:10
    • (2009) International Journal of Climatology , vol.29 , pp. 1381-1394
    • Chen, W.1
  • 8
    • 24944573867 scopus 로고    scopus 로고
    • Long-term prediction of discharges in Manwan Reservoir using artificial neural network models
    • Berlin: Springer
    • C.Cheng, et al., 2005. Long-term prediction of discharges in Manwan Reservoir using artificial neural network models. In:J. Wang, X.-F. Liao, and Z. Yi, eds. Advances in neural networks–ISNN 2005. Berlin:Springer, 1040–1045.
    • (2005) Advances in neural networks–ISNN 2005 , pp. 1040-1045
    • Cheng, C.1
  • 9
    • 84961390532 scopus 로고    scopus 로고
    • Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for the prediction of precipitation based on large-scale climate signals
    • B.Choubin, et al., 2014. Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for the prediction of precipitation based on large-scale climate signals. Hydrological Sciences Journal. doi:10.1080/02626667.2014.966721
    • (2014) Hydrological Sciences Journal
    • Choubin, B.1
  • 10
    • 27944503514 scopus 로고    scopus 로고
    • Generalized regression neural network in monthly flow forecasting
    • H.K.Cigizoglu, 2005. Generalized regression neural network in monthly flow forecasting. Civil Engineering and Environmental Systems, 22 (2), 71–81. doi:10.1080/10286600500126256
    • (2005) Civil Engineering and Environmental Systems , vol.22 , Issue.2 , pp. 71-81
    • Cigizoglu, H.K.1
  • 11
    • 84949115981 scopus 로고    scopus 로고
    • Assessing the impact of El Niño Modoki on seasonal precipitation in Colombia
    • S.Córdoba-Machado, et al., 2015. Assessing the impact of El Niño Modoki on seasonal precipitation in Colombia. Global and Planetary Change, 124, 41–61. doi:10.1016/j.gloplacha.2014.11.003
    • (2015) Global and Planetary Change , vol.124 , pp. 41-61
    • Córdoba-Machado, S.1
  • 12
    • 0034621379 scopus 로고    scopus 로고
    • Daily reservoir inflow forecasting using artificial neural networks with stopped training approach
    • P.Coulibaly, F.Anctil, and B.Bobée, 2000. Daily reservoir inflow forecasting using artificial neural networks with stopped training approach. Journal of Hydrology, 230 (3–4), 244–257. doi:10.1016/S0022-1694(00)00214-6
    • (2000) Journal of Hydrology , vol.230 , Issue.3-4 , pp. 244-257
    • Coulibaly, P.1    Anctil, F.2    Bobée, B.3
  • 13
    • 77954144514 scopus 로고    scopus 로고
    • Application of ANN and ANFIS models for reconstructing missing flow data
    • M.T.Dastorani, et al., 2010. Application of ANN and ANFIS models for reconstructing missing flow data. Environmental Monitoring and Assessment, 166 (1–4), 421–434. doi:10.1007/s10661-009-1012-8
    • (2010) Environmental Monitoring and Assessment , vol.166 , Issue.1-4 , pp. 421-434
    • Dastorani, M.T.1
  • 14
    • 0032005702 scopus 로고    scopus 로고
    • An artificial neural network approach to rainfall-runoff modelling
    • C.W.Dawson, and R.Wilby, 1998. An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal, 43, 47–66. doi:10.1080/02626669809492102
    • (1998) Hydrological Sciences Journal , vol.43 , pp. 47-66
    • Dawson, C.W.1    Wilby, R.2
  • 15
    • 0035874457 scopus 로고    scopus 로고
    • The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S
    • D.B.Enfield, A.M.Mestas-Nuñez, and P.J.Trimble, 2001. The Atlantic Multidecadal Oscillation and its relation to rainfall and river flows in the continental U.S. Geophysical Research Letters, 28 (10), 2077–2080. doi:10.1029/2000GL012745
    • (2001) Geophysical Research Letters , vol.28 , Issue.10 , pp. 2077-2080
    • Enfield, D.B.1    Mestas-Nuñez, A.M.2    Trimble, P.J.3
  • 16
    • 82155179310 scopus 로고    scopus 로고
    • Agricultural drought forecasting using satellite images, climate indices and artificial neural network
    • A.Fatehi Marj, and A.M.J.Meijerink, 2011. Agricultural drought forecasting using satellite images, climate indices and artificial neural network. International Journal of Remote Sensing, 32 (24), 9707–9719. doi:10.1080/01431161.2011.575896
    • (2011) International Journal of Remote Sensing , vol.32 , Issue.24 , pp. 9707-9719
    • Fatehi Marj, A.1    Meijerink, A.M.J.2
  • 17
    • 79956205356 scopus 로고    scopus 로고
    • Tsunami run-up height forecasting by using artificial neural networks
    • K.Günaydın, and A.Günaydın, 2011. Tsunami run-up height forecasting by using artificial neural networks. Civil Engineering and Environmental Systems, 28 (2), 165–181. doi:10.1080/10286608.2010.526703
    • (2011) Civil Engineering and Environmental Systems , vol.28 , Issue.2 , pp. 165-181
    • Günaydın, K.1    Günaydın, A.2
  • 18
    • 84863835985 scopus 로고    scopus 로고
    • Neural network based seasonal predictions of lake-effect snowfall
    • H.Hartmann, 2012. Neural network based seasonal predictions of lake-effect snowfall. Atmosphere- Ocean, 50 (1), 31–41. doi:10.1080/07055900.2012.657153
    • (2012) Atmosphere- Ocean , vol.50 , Issue.1 , pp. 31-41
    • Hartmann, H.1
  • 19
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • K.Hornik, M.Stinchcombe, and H.White, 1989. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5), 359–366. doi:10.1016/0893-6080(89)90020-8
    • (1989) Neural Networks , vol.2 , Issue.5 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 20
    • 84931565528 scopus 로고    scopus 로고
    • Monthly and seasonal drought forecasting using statistical neural networks
    • S.M.Hosseini-Moghari, and S.Araghinejad, 2015. Monthly and seasonal drought forecasting using statistical neural networks. Environmental Earth Sciences, 74, 397–412. doi:10.1007/s12665-015-4047-x
    • (2015) Environmental Earth Sciences , vol.74
    • Hosseini-Moghari, S.M.1    Araghinejad, S.2
  • 21
    • 0029413797 scopus 로고
    • Artificial neural network modeling of the rainfall-runoff process
    • K.-L.Hsu, H.V.Gupta, and S.Sorooshian, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research, 31 (10), 2517–2530. doi:10.1029/95WR01955
    • (1995) Water Resources Research , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.-L.1    Gupta, H.V.2    Sorooshian, S.3
  • 22
    • 34547265730 scopus 로고    scopus 로고
    • Rainfall-runoff modeling using principal component analysis and neural network
    • T.Hu, F.Wu, and X.Zhang, 2007. Rainfall-runoff modeling using principal component analysis and neural network. Nordic Hydrology, 38 (3), 235–248. doi:10.2166/nh.2007.010
    • (2007) Nordic Hydrology , vol.38 , Issue.3 , pp. 235-248
    • Hu, T.1    Wu, F.2    Zhang, X.3
  • 23
    • 0029479274 scopus 로고
    • Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation
    • J.W.Hurrell, 1995. Decadal trends in the North Atlantic Oscillation:regional temperatures and precipitation. Science, 269 (5224), 676–679. doi:10.1126/science.269.5224.676
    • (1995) Science , vol.269 , Issue.5224 , pp. 676-679
    • Hurrell, J.W.1
  • 24
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • J.Jang, 1993. ANFIS:adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23 (3), 665–685. doi:10.1109/21.256541
    • (1993) IEEE Transactions on Systems, Man and Cybernetics , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.1
  • 25
    • 84979995951 scopus 로고    scopus 로고
    • Hazard assessment of desertification as a result of soil and water recourse degradation in Kashan Region, Iran
    • H.Khosravi, et al., 2014. Hazard assessment of desertification as a result of soil and water recourse degradation in Kashan Region, Iran. Desert, 19, 45–55.
    • (2014) Desert , vol.19 , pp. 45-55
    • Khosravi, H.1
  • 26
    • 0032845493 scopus 로고    scopus 로고
    • HyFIS: adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems
    • J.Kim, and N.Kasabov, 1999. HyFIS:adaptive neuro-fuzzy inference systems and their application to nonlinear dynamical systems. Neural Networks, 12, 1301–1319. doi:10.1016/S0893-6080(99)00067-2
    • (1999) Neural Networks , vol.12 , pp. 1301-1319
    • Kim, J.1    Kasabov, N.2
  • 27
    • 60849126964 scopus 로고    scopus 로고
    • Adaptive neuro-fuzzy computing technique for suspended sediment estimation
    • O.Kisi, et al., S. 2009. Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Advances in Engineering Software, 40 (6), 438–444.
    • (2009) Advances in Engineering Software , vol.40 , Issue.6 , pp. 438-444
    • Kisi, O.1
  • 28
    • 84939249695 scopus 로고    scopus 로고
    • One-day-ahead streamflow forecasting using artificial neural networks and a meteorological mesoscale model
    • A.Linares-Rodriguez, et al., 2015. One-day-ahead streamflow forecasting using artificial neural networks and a meteorological mesoscale model. Journal of Hydrologic Engineering, 20, 05015001. doi:10.1061/(ASCE)HE.1943-5584.0001163
    • (2015) Journal of Hydrologic Engineering , vol.20
    • Linares-Rodriguez, A.1
  • 29
    • 51649097829 scopus 로고    scopus 로고
    • PCA and SVD with nonnegative loadings
    • S.Lipovetsky, 2009. PCA and SVD with nonnegative loadings. Pattern Recognition, 42 (1), 68–76. doi:10.1016/j.patcog.2008.06.025
    • (2009) Pattern Recognition , vol.42 , Issue.1 , pp. 68-76
    • Lipovetsky, S.1
  • 30
    • 70350129932 scopus 로고    scopus 로고
    • Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods
    • M.Mamak, et al., 2009. Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods. Civil Engineering and Environmental Systems, 26 (3), 279–293. doi:10.1080/10286600802151804
    • (2009) Civil Engineering and Environmental Systems , vol.26 , Issue.3 , pp. 279-293
    • Mamak, M.1
  • 31
    • 33751428182 scopus 로고    scopus 로고
    • Singular spectrum analysis and forecasting of hydrological time series
    • C.A.F.Marques, et al., 2006. Singular spectrum analysis and forecasting of hydrological time series. Physics and Chemistry of the Earth, Parts A/B/C, 31 (18), 1172–1179. doi:10.1016/j.pce.2006.02.061
    • (2006) Physics and Chemistry of the Earth, Parts A/B/C , vol.31 , Issue.18 , pp. 1172-1179
    • Marques, C.A.F.1
  • 32
    • 79952299253 scopus 로고    scopus 로고
    • Atlantic and Pacific sea surface temperatures and corn yields in the southeastern USA: lagged relationships and forecast model development
    • C.J.Martinez, and J.W.Jones, 2011. Atlantic and Pacific sea surface temperatures and corn yields in the southeastern USA:lagged relationships and forecast model development. International Journal of Climatology, 31, 592–604. doi:10.1002/joc.v31.4
    • (2011) International Journal of Climatology , vol.31 , pp. 592-604
    • Martinez, C.J.1    Jones, J.W.2
  • 33
    • 59549104503 scopus 로고    scopus 로고
    • Analysing streamflow variability and water allocation for sustainable management of water resources in the semi-arid Karkheh river basin, Iran
    • I.Masih, et al., 2009. Analysing streamflow variability and water allocation for sustainable management of water resources in the semi-arid Karkheh river basin, Iran. Physics and Chemistry of the Earth, 34, 329–340. doi:10.1016/j.pce.2008.09.006
    • (2009) Physics and Chemistry of the Earth , vol.34 , pp. 329-340
    • Masih, I.1
  • 34
    • 33750742025 scopus 로고    scopus 로고
    • Analysis of ANFIS model for polymerization process
    • Gabrys B., Howlett R.-J., Jain L.-C., (eds), Berlin: Springer
    • H.Matsumoto, C.Lin, and C.Kuroda, 2006. Analysis of ANFIS model for polymerization process. In:B.Gabrys, R.-J.Howlett, and L.-C.Jain, eds. Knowledge-based intelligent information and engineering systems. Berlin:Springer, 561–568.
    • (2006) Knowledge-based intelligent information and engineering systems , pp. 561-568
    • Matsumoto, H.1    Lin, C.2    Kuroda, C.3
  • 35
    • 33747853860 scopus 로고    scopus 로고
    • Drought forecasting using feed-forward recursive neural network
    • A.K.Mishra, & V.R.Desai, 2006. Drought forecasting using feed-forward recursive neural network. Ecological Modelling, 198 (1–5), 127–138.
    • (2006) Ecological Modelling , vol.198 , Issue.1-5 , pp. 127-138
    • Mishra, A.K.1    Desai, V.R.2
  • 36
    • 37249024658 scopus 로고    scopus 로고
    • Drought forecasting using artificial neural networks and time series of drought indices
    • S.Morid, V.Smakhtin, and K.Bagherzadeh, 2007. Drought forecasting using artificial neural networks and time series of drought indices. International Journal of Climatology, 27, 2103–2111. doi:10.1002/(ISSN)1097-0088
    • (2007) International Journal of Climatology , vol.27 , pp. 2103-2111
    • Morid, S.1    Smakhtin, V.2    Bagherzadeh, K.3
  • 37
    • 33845384467 scopus 로고    scopus 로고
    • Neural network and genetic programming for modelling coastal algal blooms
    • N.Muttil, and K.W.Chau, 2006. Neural network and genetic programming for modelling coastal algal blooms. International Journal of Environment and Pollution, 28 (3/4), 223–238. doi:10.1504/IJEP.2006.011208
    • (2006) International Journal of Environment and Pollution , vol.28 , Issue.3-4 , pp. 223-238
    • Muttil, N.1    Chau, K.W.2
  • 38
    • 0014776873 scopus 로고
    • River flow forecasting through conceptual models part I — a discussion of principles
    • J.E.Nash, and J.V.Sutcliffe, 1970. River flow forecasting through conceptual models part I — a discussion of principles. Journal of Hydrology, 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6
    • (1970) Journal of Hydrology , vol.10 , Issue.3 , pp. 282-290
    • Nash, J.E.1    Sutcliffe, J.V.2
  • 40
    • 34447527322 scopus 로고    scopus 로고
    • Wavelet and neuro-fuzzy conjunction model for precipitation forecasting
    • T.Partal, and Ö.Kişi, 2007. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology, 342 (1–2), 199–212. doi:10.1016/j.jhydrol.2007.05.026
    • (2007) Journal of Hydrology , vol.342 , Issue.1-2 , pp. 199-212
    • Partal, T.1    Kişi, Ö.2
  • 41
    • 69949143939 scopus 로고    scopus 로고
    • Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction
    • N.Pramanik, & R.K.Panda, 2009. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrological Sciences Journal, 54 (2), 247–260.
    • (2009) Hydrological Sciences Journal , vol.54 , Issue.2 , pp. 247-260
    • Pramanik, N.1    Panda, R.K.2
  • 42
    • 84920431776 scopus 로고    scopus 로고
    • Wavelet-ANFIS models for forecasting monsoon flows: case study for the Gandak River (India)
    • R.R.Sahay, and V.Sehgal, 2014. Wavelet-ANFIS models for forecasting monsoon flows:case study for the Gandak River (India). Water Resources, 41 (5), 574–582. doi:10.1134/S0097807814050108
    • (2014) Water Resources , vol.41 , Issue.5 , pp. 574-582
    • Sahay, R.R.1    Sehgal, V.2
  • 43
    • 84947036007 scopus 로고    scopus 로고
    • Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran)
    • (just-accepted)
    • F.Sarani, A.G.Ahangar, and A.Shabani, 2015. Predicting ESP and SAR by artificial neural network and regression models using soil pH and EC data (Miankangi Region, Sistan and Baluchestan Province, Iran). Archives of Agronomy and Soil Science, (just-accepted). 1–12. doi:10.1080/03650340.2015.1040398
    • (2015) Archives of Agronomy and Soil Science
    • Sarani, F.1    Ahangar, A.G.2    Shabani, A.3
  • 44
    • 84910653625 scopus 로고    scopus 로고
    • Performance comparison of Adoptive Neuro Fuzzy Inference System (ANFIS) with Loading Simulation Program C++ (LSPC) model for streamflow simulation in El Niño Southern Oscillation (ENSO)-affected watershed
    • S.Sharma, et al., 2015. Performance comparison of Adoptive Neuro Fuzzy Inference System (ANFIS) with Loading Simulation Program C++ (LSPC) model for streamflow simulation in El Niño Southern Oscillation (ENSO)-affected watershed. Expert Systems with Applications, 42, 2213–2223. doi:10.1016/j.eswa.2014.09.062
    • (2015) Expert Systems with Applications , vol.42 , pp. 2213-2223
    • Sharma, S.1
  • 45
    • 84912068420 scopus 로고    scopus 로고
    • Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran
    • S.K.Sigaroodi, et al., 2013. Long-term precipitation forecast for drought relief using atmospheric circulation factors:a study on the Maharloo Basin in Iran. Hydrology and Earth System Sciences Discussions, 10, 13333–13361. doi:10.5194/hessd-10-13333-2013
    • (2013) Hydrology and Earth System Sciences Discussions , vol.10 , pp. 13333-13361
    • Sigaroodi, S.K.1
  • 46
    • 84900441306 scopus 로고    scopus 로고
    • Metal oxide SAW E-nose employing PCA and ANN for the identification of binary mixture of DMMP and methanol
    • H.Singh, et al., 2014. Metal oxide SAW E-nose employing PCA and ANN for the identification of binary mixture of DMMP and methanol. Sensors and Actuators B:Chemical, 200, 147–156. doi:10.1016/j.snb.2014.04.065
    • (2014) Sensors and Actuators B: Chemical , vol.200 , pp. 147-156
    • Singh, H.1
  • 47
    • 33846453985 scopus 로고    scopus 로고
    • Genetic programming model for forecast of short and noisy data
    • C.Sivapragasam, P.Vincent, and G.Vasudevan, 2007. Genetic programming model for forecast of short and noisy data. Hydrological Processes, 21 (2), 266–272. doi:10.1002/(ISSN)1099-1085
    • (2007) Hydrological Processes , vol.21 , Issue.2 , pp. 266-272
    • Sivapragasam, C.1    Vincent, P.2    Vasudevan, G.3
  • 48
    • 84919329541 scopus 로고    scopus 로고
    • Streamflow forecasting for operational water management in the Incomati River Basin, Southern Africa
    • R.K.M.Sunday, et al., 2014. Streamflow forecasting for operational water management in the Incomati River Basin, Southern Africa. Physics and Chemistry of the Earth, 72-75, 1–12. doi:10.1016/j.pce.2014.09.002
    • (2014) Physics and Chemistry of the Earth , vol.72-75 , pp. 1-12
    • Sunday, R.K.M.1
  • 49
    • 84924153739 scopus 로고    scopus 로고
    • Neural network river forecasting with multi-objective fully informed particle swarm optimization
    • R.Taormina, and K.-W.Chau, 2015. Neural network river forecasting with multi-objective fully informed particle swarm optimization. Journal of Hydroinformatics, 17 (1), 99–113. doi:10.2166/hydro.2014.116
    • (2015) Journal of Hydroinformatics , vol.17 , Issue.1 , pp. 99-113
    • Taormina, R.1    Chau, K.-W.2
  • 50
    • 0034694775 scopus 로고    scopus 로고
    • Comparison of short-term rainfall prediction models for real-time flood forecasting
    • E.Toth, A.Brath, and A.Montanari, 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology, 239 (1–4), 132–147. doi:10.1016/S0022-1694(00)00344-9
    • (2000) Journal of Hydrology , vol.239 , Issue.1-4 , pp. 132-147
    • Toth, E.1    Brath, A.2    Montanari, A.3
  • 52
    • 33644891019 scopus 로고    scopus 로고
    • Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system
    • B.Tutmez, Z.Hatipoglu, and U.Kaymak, 2006. Modelling electrical conductivity of groundwater using an adaptive neuro-fuzzy inference system. Computers & Geosciences, 32 (4), 421–433. doi:10.1016/j.cageo.2005.07.003
    • (2006) Computers & Geosciences , vol.32 , Issue.4 , pp. 421-433
    • Tutmez, B.1    Hatipoglu, Z.2    Kaymak, U.3
  • 53
    • 0000634523 scopus 로고
    • Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies
    • J.M.Wallace, D.S.Gutzler, and C.S.Bretheron, 1992. Singular value decomposition of wintertime sea surface temperature and 500-mb height anomalies. Journal of Climate, 5, 561–576. doi:10.1175/1520-0442(1992)005<0561:SVDOWS>2.0.CO;2
    • (1992) Journal of Climate , vol.5 , pp. 561-576
    • Wallace, J.M.1    Gutzler, D.S.2    Bretheron, C.S.3
  • 54
    • 68349105875 scopus 로고    scopus 로고
    • A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
    • W.-C.Wang, et al., 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology, 374 (3–4), 294–306. doi:10.1016/j.jhydrol.2009.06.019
    • (2009) Journal of Hydrology , vol.374 , Issue.3-4 , pp. 294-306
    • Wang, W.-C.1
  • 55
    • 77958035437 scopus 로고    scopus 로고
    • Data-driven models for monthly streamflow time series prediction
    • C.L.Wu, and K.W.Chau, 2010. Data-driven models for monthly streamflow time series prediction. Engineering Applications of Artificial Intelligence, 23 (8), 1350–1367. doi:10.1016/j.engappai.2010.04.003
    • (2010) Engineering Applications of Artificial Intelligence , vol.23 , Issue.8 , pp. 1350-1367
    • Wu, C.L.1    Chau, K.W.2
  • 56
    • 77954384622 scopus 로고    scopus 로고
    • Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques
    • C.L.Wu, K.W.Chau, and C.Fan, 2010. Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. Journal of Hydrology, 389 (1–2), 146–167. doi:10.1016/j.jhydrol.2010.05.040
    • (2010) Journal of Hydrology , vol.389 , Issue.1-2 , pp. 146-167
    • Wu, C.L.1    Chau, K.W.2    Fan, C.3
  • 57
    • 47949121319 scopus 로고    scopus 로고
    • River stage prediction based on a distributed support vector regression
    • C.L.Wu, K.W.Chau, and Y.S.Li, 2008. River stage prediction based on a distributed support vector regression. Journal of Hydrology, 358 (1–2), 96–111. doi:10.1016/j.jhydrol.2008.05.028
    • (2008) Journal of Hydrology , vol.358 , Issue.1-2 , pp. 96-111
    • Wu, C.L.1    Chau, K.W.2    Li, Y.S.3
  • 58
    • 65749118118 scopus 로고    scopus 로고
    • Methods to improve neural network performance in daily flows prediction
    • C.L.Wu, K.W.Chau, and Y.S.Li, 2009. Methods to improve neural network performance in daily flows prediction. Journal of Hydrology, 372 (1–4), 80–93. doi:10.1016/j.jhydrol.2009.03.038
    • (2009) Journal of Hydrology , vol.372 , Issue.1-4 , pp. 80-93
    • Wu, C.L.1    Chau, K.W.2    Li, Y.S.3
  • 59
    • 0030731329 scopus 로고    scopus 로고
    • ENSO-like interdecadal variability: 1900–93
    • Y.Zhang, J.M.Wallace, and D.S.Battisti, 1997. ENSO-like interdecadal variability:1900–93. Journal of Climate, 10, 1004–1020. doi:10.1175/1520-0442(1997)010<1004:ELIV>2.0.CO;2
    • (1997) Journal of Climate , vol.10 , pp. 1004-1020
    • Zhang, Y.1    Wallace, J.M.2    Battisti, D.S.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.