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Volumn 495, Issue , 2013, Pages 52-63

Development of an effective data-driven model for hourly typhoon rainfall forecasting

Author keywords

Meteorological parameters; Multi objective genetic algorithm; Support vector machine; Typhoon rainfall forecasting

Indexed keywords

DATA-DRIVEN MODEL; INPUT VARIABLES; LOW-ALTITUDE; METEOROLOGICAL PARAMETERS; MULTI-OBJECTIVE GENETIC ALGORITHM; OPTIMAL COMBINATION; RAINFALL DATA; TYPHOON RAINFALL;

EID: 84878439205     PISSN: 00221694     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jhydrol.2013.04.050     Document Type: Article
Times cited : (42)

References (38)
  • 1
    • 84865047835 scopus 로고    scopus 로고
    • Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data
    • Adamowski J., Chan H.F., Prasher S.O., Sharda V.N. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data. J. Hydroinform. 2012, 14(3):731-744.
    • (2012) J. Hydroinform. , vol.14 , Issue.3 , pp. 731-744
    • Adamowski, J.1    Chan, H.F.2    Prasher, S.O.3    Sharda, V.N.4
  • 2
    • 0034174280 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. I: preliminary concepts
    • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology
    • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. I: preliminary concepts. J. Hydrol. Eng. 2000, 5(2):115-123.
    • (2000) J. Hydrol. Eng. , vol.5 , Issue.2 , pp. 115-123
  • 3
    • 0034174396 scopus 로고    scopus 로고
    • Artificial neural networks in hydrology. II: hydrologic applications
    • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology
    • ASCE Task Committee on Application of Artificial Neural Networks in Hydrology Artificial neural networks in hydrology. II: hydrologic applications. J. Hydrol. Eng. 2000, 5(2):124-137.
    • (2000) J. Hydrol. Eng. , vol.5 , Issue.2 , pp. 124-137
  • 4
    • 33846847847 scopus 로고    scopus 로고
    • Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique
    • Bae D.H., Jeong D.M., Kim G. Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique. Hydrol. Sci. J.-J. Sci. Hydrol. 2007, 52(1):99-113.
    • (2007) Hydrol. Sci. J.-J. Sci. Hydrol. , vol.52 , Issue.1 , pp. 99-113
    • Bae, D.H.1    Jeong, D.M.2    Kim, G.3
  • 5
    • 68749110869 scopus 로고    scopus 로고
    • Auto-configuring radial basis function networks for chaotic time series and flood forecasting
    • Chang L.C., Chang F.J., Wang Y.P. Auto-configuring radial basis function networks for chaotic time series and flood forecasting. Hydrol. Process. 2009, 23(17):2450-2459.
    • (2009) Hydrol. Process. , vol.23 , Issue.17 , pp. 2450-2459
    • Chang, L.C.1    Chang, F.J.2    Wang, Y.P.3
  • 6
    • 34447256610 scopus 로고    scopus 로고
    • Feed forward artificial neural network model to predict the average summer monsoon rainfall in India
    • Chattopadhyay S. Feed forward artificial neural network model to predict the average summer monsoon rainfall in India. Acta Geophys. 2007, 55(3):369-382.
    • (2007) Acta Geophys. , vol.55 , Issue.3 , pp. 369-382
    • Chattopadhyay, S.1
  • 7
    • 41649089759 scopus 로고    scopus 로고
    • Identification of the best hidden layer size for three-layered neural net in predicting monsoon rainfall in India
    • Chattopadhyay S., Chattopadhyay G. Identification of the best hidden layer size for three-layered neural net in predicting monsoon rainfall in India. J. Hydroinform. 2008, 10(2):181-188.
    • (2008) J. Hydroinform. , vol.10 , Issue.2 , pp. 181-188
    • Chattopadhyay, S.1    Chattopadhyay, G.2
  • 8
    • 78049358493 scopus 로고    scopus 로고
    • A hybrid model coupled with singular spectrum analysis for daily rainfall prediction
    • Chau K.W., Wu C.L. A hybrid model coupled with singular spectrum analysis for daily rainfall prediction. J. Hydroinform. 2010, 12(4):458-473.
    • (2010) J. Hydroinform. , vol.12 , Issue.4 , pp. 458-473
    • Chau, K.W.1    Wu, C.L.2
  • 10
    • 20344369583 scopus 로고    scopus 로고
    • Groundwater level forecasting using artificial neural networks
    • Daliakopoulos I.N., Coulibaly P., Tsanis I.K. Groundwater level forecasting using artificial neural networks. J. Hydrol. 2005, 309(1-4):229-240.
    • (2005) J. Hydrol. , vol.309 , Issue.1-4 , pp. 229-240
    • Daliakopoulos, I.N.1    Coulibaly, P.2    Tsanis, I.K.3
  • 12
    • 33846428782 scopus 로고    scopus 로고
    • Potential assessment of the support vector regression technique in rainfall forecasting
    • Hong W.C., Pai P.F. Potential assessment of the support vector regression technique in rainfall forecasting. Water Resour. Manage. 2007, 21(2):495-513.
    • (2007) Water Resour. Manage. , vol.21 , Issue.2 , pp. 495-513
    • Hong, W.C.1    Pai, P.F.2
  • 13
    • 77954958168 scopus 로고    scopus 로고
    • Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting
    • Lin G.F., Chen G.R., Huang P.Y. Effective typhoon characteristics and their effects on hourly reservoir inflow forecasting. Adv. Water Resour. 2010, 33(8):887-898. 10.1016/j.advwatres.2010.04.016.
    • (2010) Adv. Water Resour. , vol.33 , Issue.8 , pp. 887-898
    • Lin, G.F.1    Chen, G.R.2    Huang, P.Y.3
  • 14
    • 65649123113 scopus 로고    scopus 로고
    • Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods
    • Lin G.F., Chen G.R., Huang P.Y., Chou Y.C. Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods. J. Hydrol. 2009, 372(1-4):17-29. 10.1016/j.jhydrol.2009.03.032.
    • (2009) J. Hydrol. , vol.372 , Issue.1-4 , pp. 17-29
    • Lin, G.F.1    Chen, G.R.2    Huang, P.Y.3    Chou, Y.C.4
  • 15
    • 70349774410 scopus 로고    scopus 로고
    • Effective forecasting of hourly typhoon rainfall using support vector machines
    • Lin G.F., Chen G.R., Wu M.C., Chou Y.C. Effective forecasting of hourly typhoon rainfall using support vector machines. Water Resour. Res. 2009, 45(8):W08440. 10.1029/2009WR007911.
    • (2009) Water Resour. Res. , vol.45 , Issue.8
    • Lin, G.F.1    Chen, G.R.2    Wu, M.C.3    Chou, Y.C.4
  • 16
    • 1642387025 scopus 로고    scopus 로고
    • A non-linear rainfall-runoff model using radial basis function network
    • Lin G.F., Chen L.H. A non-linear rainfall-runoff model using radial basis function network. J. Hydrol. 2004, 289(1-4):1-8. 10.1016/j.jhydrol.2003.10.015.
    • (2004) J. Hydrol. , vol.289 , Issue.1-4 , pp. 1-8
    • Lin, G.F.1    Chen, L.H.2
  • 17
    • 20844462859 scopus 로고    scopus 로고
    • Application of artificial neural network to typhoon rainfall forecasting
    • Lin G.F., Chen L.H. Application of artificial neural network to typhoon rainfall forecasting. Hydrol. Process. 2005, 19(9):1825-1837. 10.1002/hyp.5638.
    • (2005) Hydrol. Process. , vol.19 , Issue.9 , pp. 1825-1837
    • Lin, G.F.1    Chen, L.H.2
  • 18
    • 84875550980 scopus 로고    scopus 로고
    • Typhoon flood forecasting using integrated two-stage support vector machine approach
    • Lin G.F., Chou Y.C., Wu M.C. Typhoon flood forecasting using integrated two-stage support vector machine approach. J. Hydrol. 2013, 486:334-342. 10.1016/j.jhydrol.2013.02.012.
    • (2013) J. Hydrol. , vol.486 , pp. 334-342
    • Lin, G.F.1    Chou, Y.C.2    Wu, M.C.3
  • 19
    • 69349084510 scopus 로고    scopus 로고
    • A hybrid neural network model for typhoon-rainfall forecasting
    • Lin G.F., Wu M.C. A hybrid neural network model for typhoon-rainfall forecasting. J. Hydrol. 2009, 375(3-4):450-458. 10.1016/j.jhydrol.2009.06.047.
    • (2009) J. Hydrol. , vol.375 , Issue.3-4 , pp. 450-458
    • Lin, G.F.1    Wu, M.C.2
  • 20
    • 0036202123 scopus 로고    scopus 로고
    • Flood stage forecasting with support vector machines
    • Liong S.Y., Sivapragasam C. Flood stage forecasting with support vector machines. J. Am. Water Resour. Assoc. 2002, 38(1):173-186.
    • (2002) J. Am. Water Resour. Assoc. , vol.38 , Issue.1 , pp. 173-186
    • Liong, S.Y.1    Sivapragasam, C.2
  • 21
    • 79960705269 scopus 로고    scopus 로고
    • Application of a genetic algorithm for the optimization of a complex reservoir system in Tunisia
    • Louati M.H., Benabdallah S., Lebdi F., Milutin D. Application of a genetic algorithm for the optimization of a complex reservoir system in Tunisia. Water Resour. Manage. 2011, 25(10):2387-2404.
    • (2011) Water Resour. Manage. , vol.25 , Issue.10 , pp. 2387-2404
    • Louati, M.H.1    Benabdallah, S.2    Lebdi, F.3    Milutin, D.4
  • 22
    • 0034737033 scopus 로고    scopus 로고
    • A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting
    • Luk K.C., Ball J.E., Sharma A. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. J. Hydrol. 2000, 227(1-4):56-65.
    • (2000) J. Hydrol. , vol.227 , Issue.1-4 , pp. 56-65
    • Luk, K.C.1    Ball, J.E.2    Sharma, A.3
  • 23
    • 0035104376 scopus 로고    scopus 로고
    • An application of artificial neural networks for rainfall forecasting
    • Luk K.C., Ball J.E., Sharma A. An application of artificial neural networks for rainfall forecasting. Math. Comput. Model. 2001, 33(6-7):683-693.
    • (2001) Math. Comput. Model. , vol.33 , Issue.6-7 , pp. 683-693
    • Luk, K.C.1    Ball, J.E.2    Sharma, A.3
  • 24
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications
    • Maier H.R., Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modeling issues and applications. Environ. Modell. Softw. 2000, 15(1):101-124.
    • (2000) Environ. Modell. Softw. , vol.15 , Issue.1 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 25
    • 77951662436 scopus 로고    scopus 로고
    • Potential of support vector regression for prediction of monthly streamflow using endogenous property
    • Maity R., Bhagwat P.P., Bhatnagar A. Potential of support vector regression for prediction of monthly streamflow using endogenous property. Hydrol. Process. 2010, 24(7):917-923.
    • (2010) Hydrol. Process. , vol.24 , Issue.7 , pp. 917-923
    • Maity, R.1    Bhagwat, P.P.2    Bhatnagar, A.3
  • 26
    • 80052924174 scopus 로고    scopus 로고
    • Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction
    • Mount N.J., Abrahart R.J. Load or concentration, logged or unlogged? Addressing ten years of uncertainty in neural network suspended sediment prediction. Hydrol. Process. 2011, 25(20):3144-3157.
    • (2011) Hydrol. Process. , vol.25 , Issue.20 , pp. 3144-3157
    • Mount, N.J.1    Abrahart, R.J.2
  • 28
    • 67649122251 scopus 로고    scopus 로고
    • An ANN-based model for spatiotemporal groundwater level forecasting
    • Nourani V., Mogaddam A.A., Nadiri A.O. An ANN-based model for spatiotemporal groundwater level forecasting. Hydrol. Process. 2008, 22(26):5054-5066.
    • (2008) Hydrol. Process. , vol.22 , Issue.26 , pp. 5054-5066
    • Nourani, V.1    Mogaddam, A.A.2    Nadiri, A.O.3
  • 29
    • 10644287862 scopus 로고    scopus 로고
    • Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region
    • Ramirez M.C.V., Velho H.F.D., Ferreira N.J. Artificial neural network technique for rainfall forecasting applied to the Sao Paulo region. J. Hydrol. 2005, 301(1-4):146-162.
    • (2005) J. Hydrol. , vol.301 , Issue.1-4 , pp. 146-162
    • Ramirez, M.C.V.1    Velho, H.F.D.2    Ferreira, N.J.3
  • 30
    • 33750403562 scopus 로고    scopus 로고
    • Optimal reservoir operation using multi-objective evolutionary algorithm
    • Reddy M.J., Kumar D.N. Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour. Manage. 2006, 20(6):861-878.
    • (2006) Water Resour. Manage. , vol.20 , Issue.6 , pp. 861-878
    • Reddy, M.J.1    Kumar, D.N.2
  • 31
    • 1842787606 scopus 로고    scopus 로고
    • Striking the balance: long-term groundwater monitoring design for conflicting objectives
    • Reed P.M., Minsker B.S. Striking the balance: long-term groundwater monitoring design for conflicting objectives. J. Water Resour. Plan. Manage. -ASCE 2004, 130(2):140-149.
    • (2004) J. Water Resour. Plan. Manage. -ASCE , vol.130 , Issue.2 , pp. 140-149
    • Reed, P.M.1    Minsker, B.S.2
  • 32
    • 18144390148 scopus 로고    scopus 로고
    • Flow categorization model for improving forecasting
    • Sivapragasam C., Liong S.Y. Flow categorization model for improving forecasting. Nord Hydrol. 2005, 36(1):37-48.
    • (2005) Nord Hydrol. , vol.36 , Issue.1 , pp. 37-48
    • Sivapragasam, C.1    Liong, S.Y.2
  • 35
  • 36
    • 65449144705 scopus 로고    scopus 로고
    • Application of integrated back-propagation network and self organizing map for flood forecasting
    • Yang C.C., Chen C.S. Application of integrated back-propagation network and self organizing map for flood forecasting. Hydrol. Process. 2009, 23(9):1313-1323.
    • (2009) Hydrol. Process. , vol.23 , Issue.9 , pp. 1313-1323
    • Yang, C.C.1    Chen, C.S.2
  • 37
    • 0032579570 scopus 로고    scopus 로고
    • Multi-objective global optimization for hydrologic models
    • Yapo P.O., Gupta H.V., Sorooshian S. Multi-objective global optimization for hydrologic models. J. Hydrol. 1998, 204(1-4):83-97.
    • (1998) J. Hydrol. , vol.204 , Issue.1-4 , pp. 83-97
    • Yapo, P.O.1    Gupta, H.V.2    Sorooshian, S.3
  • 38
    • 33845702662 scopus 로고    scopus 로고
    • Forecasting of hydrologic time series with ridge regression in feature space
    • Yu X.Y., Liong S.Y. Forecasting of hydrologic time series with ridge regression in feature space. J. Hydrol. 2007, 332(3-4):290-302.
    • (2007) J. Hydrol. , vol.332 , Issue.3-4 , pp. 290-302
    • Yu, X.Y.1    Liong, S.Y.2


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