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Volumn 44, Issue 1, 2008, Pages 148-165

Uncertainty reduction of the flood stage forecasting using neural networks model

Author keywords

EDRNNM; Flood stage forecasting; Sensitivity analysis; Uncertainty reduction

Indexed keywords

DATA ACQUISITION; FORECASTING; MATHEMATICAL MODELS; NEURAL NETWORKS; SENSITIVITY ANALYSIS;

EID: 42049093796     PISSN: 1093474X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.1752-1688.2007.00144.x     Document Type: Article
Times cited : (23)

References (45)
  • 1
    • 16444369153 scopus 로고    scopus 로고
    • Information Theory and Neural Networks for Managing Uncertainty in Flood Routing
    • Abebe, A.J. R.K. Price, 2004. Information Theory and Neural Networks for Managing Uncertainty in Flood Routing. Journal of Computing in Civil Engineering, ASCE 18 (4 373 380.
    • (2004) Journal of Computing in Civil Engineering, ASCE , vol.18 , Issue.4 , pp. 373-380
    • Abebe, A.J.1    Price, R.K.2
  • 2
    • 0034174280 scopus 로고    scopus 로고
    • Artificial Neural Network in Hydrology. I: Preliminary Concepts
    • ASCE Task Committee on Application of Neural Networks in Hydrology
    • ASCE Task Committee on Application of Neural Networks in Hydrology, 2000. Artificial Neural Network in Hydrology. I: Preliminary Concepts. Journal of Hydrologic Engineering, ASCE 5 (2 115 123.
    • (2000) Journal of Hydrologic Engineering, ASCE , vol.5 , Issue.2 , pp. 115-123
  • 3
    • 0001044176 scopus 로고
    • Neural Networks and Their Applications
    • Bishop, C.M., 1994. Neural Networks and Their Applications. Review of Scientific Instruments 65 : 1803 1832.
    • (1994) Review of Scientific Instruments , vol.65 , pp. 1803-1832
    • Bishop, C.M.1
  • 4
    • 0032722662 scopus 로고    scopus 로고
    • Forecasting River Flow Rate during Lowflow Periods using Neural Networks
    • Campolo, M., P. Andreussi A. Soldati, 1999. Forecasting River Flow Rate during Lowflow Periods using Neural Networks. Water Resources Research 35 (11 3547 3552.
    • (1999) Water Resources Research , vol.35 , Issue.11 , pp. 3547-3552
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 5
    • 21744431610 scopus 로고    scopus 로고
    • Development of Recurrent Sigma-Pi Neural Network Rainfall Forecasting System in Hong Kong
    • Chow, T.W.S. S.Y. Cho, 1997. Development of Recurrent Sigma-Pi Neural Network Rainfall Forecasting System in Hong Kong. Neural Computing and Applications 5 (2 66 75.
    • (1997) Neural Computing and Applications , vol.5 , Issue.2 , pp. 66-75
    • Chow, T.W.S.1    Cho, S.Y.2
  • 6
    • 0034621379 scopus 로고    scopus 로고
    • Daily Reservoir Inflow Forecasting using Artificial Neural Networks with Stopped Training Approach
    • Coulibaly, P., F. Anctil B. Bobee, 2000. Daily Reservoir Inflow Forecasting using Artificial Neural Networks with Stopped Training Approach. Journal of Hydrology 230 : 244 257.
    • (2000) Journal of Hydrology , vol.230 , pp. 244-257
    • Coulibaly, P.1    Anctil, F.2    Bobee, B.3
  • 8
    • 26444565569 scopus 로고
    • Finding Structure in Time
    • Elman, J.L., 1990. Finding Structure in Time. Cognitive Science 14 : 179 211.
    • (1990) Cognitive Science , vol.14 , pp. 179-211
    • Elman, J.L.1
  • 9
    • 0030708475 scopus 로고    scopus 로고
    • Proceedings of 1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, IEEE Press, Piscataway, New Jersey, pp.
    • Giles, C.L., S. Lawrence A.C. Tsoi, 1997. Rule Inference for Financial Prediction using Recurrent Neural Networks. Proceedings of 1997 IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, IEEE Press, Piscataway, New Jersey, pp. 253 259.
    • (1997) Rule Inference for Financial Prediction Using Recurrent Neural Networks. , pp. 253-259
    • Giles, C.L.1    Lawrence, S.2    Tsoi, A.C.3
  • 10
    • 0026899191 scopus 로고
    • Use of Artificial Neural Networks to Analyze Nuclear Plant Performance
    • Guo, Z. R.E. Uhrig, 1992. Use of Artificial Neural Networks to Analyze Nuclear Plant Performance. Nuclear Technology 99 : 36 42.
    • (1992) Nuclear Technology , vol.99 , pp. 36-42
    • Guo, Z.1    Uhrig, R.E.2
  • 14
    • 0003413187 scopus 로고
    • Macmillan College Publication Company Incorporated, New York, New York.
    • Haykin, S., 1994. Neural Networks : A Comprehensive Foundation. Macmillan College Publication Company Incorporated, New York, New York.
    • (1994) Neural Networks : A Comprehensive Foundation.
    • Haykin, S.1
  • 15
    • 0029413797 scopus 로고
    • Artificial Neural Network Modeling of the Rainfall-Runoff Process
    • Hsu, K., V.H. Gupta S. Sorooshian, 1995. Artificial Neural Network Modeling of the Rainfall-Runoff Process. Water Resources Research 31 (10 2517 2530.
    • (1995) Water Resources Research , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.1    Gupta, V.H.2    Sorooshian, S.3
  • 16
    • 28844473522 scopus 로고    scopus 로고
    • Integrated Approach to Model Decomposed Flow Hydrograph using Artificial Neural Network and Conceptual Techniques
    • Jain, A. S. Srinivasulu, 2006. Integrated Approach to Model Decomposed Flow Hydrograph using Artificial Neural Network and Conceptual Techniques. Journal of Hydrology 317 : 291 306.
    • (2006) Journal of Hydrology , vol.317 , pp. 291-306
    • Jain, A.1    Srinivasulu, S.2
  • 17
    • 14644411776 scopus 로고    scopus 로고
    • Determination of an Optimal Unit Pulse Response Function using Real-Coded Genetic Algorithm
    • Jain, A., S. Srinivasulu R.K. Bhattacharjya, 2005. Determination of an Optimal Unit Pulse Response Function using Real-Coded Genetic Algorithm. Journal of Hydrology 303 : 199 241.
    • (2005) Journal of Hydrology , vol.303 , pp. 199-241
    • Jain, A.1    Srinivasulu, S.2    Bhattacharjya, R.K.3
  • 23
    • 0034641121 scopus 로고    scopus 로고
    • River Flow Prediction using Artificial Neural Networks : GGGGeneralized beyond Calibration Range
    • Korre, A., S. Durucan C.E. Imrie, 2000. River Flow Prediction using Artificial Neural Networks : Generalized beyond Calibration Range. Journal of Hydrology 233 : 138 153.
    • (2000) Journal of Hydrology , vol.233 , pp. 138-153
    • Korre, A.1    Durucan, S.2    Imrie, C.E.3
  • 25
    • 0024765401 scopus 로고
    • Analysis and Synthesis of a Class of Neural Networks : LLLLinear Systems Operating on a Closed Hypercube
    • Li, J., A.N. Michel W. Porod, 1989. Analysis and Synthesis of a Class of Neural Networks : Linear Systems Operating on a Closed Hypercube. IEEE Transactions on Circuits and Systems 36 (11 1405 1422.
    • (1989) IEEE Transactions on Circuits and Systems , vol.36 , Issue.11 , pp. 1405-1422
    • Li, J.1    Michel, A.N.2    Porod, W.3
  • 27
    • 0035499011 scopus 로고    scopus 로고
    • Uncertainty Assessment of Regionalized Flood Frequency Estimates
    • Michele, C.D. R. Rosso, 2001. Uncertainty Assessment of Regionalized Flood Frequency Estimates. Journal of Hydrologic Engineering, ASCE 6 (6 453 459.
    • (2001) Journal of Hydrologic Engineering, ASCE , vol.6 , Issue.6 , pp. 453-459
    • Michele, C.D.1    Rosso, R.2
  • 29
    • 0008589241 scopus 로고
    • Flood Forecasting Model for Citandy River
    • In. V.P. Singh (. Editor). Reidel, Dordrecht, The Netherlands, pp.
    • Mutreja, K.N., A. Yin I. Martino, 1987. Flood Forecasting Model for Citandy River. In Flood Hydrology, V.P. Singh (Editor). Reidel, Dordrecht, The Netherlands, pp. 211 220.
    • (1987) Flood Hydrology , pp. 211-220
    • Mutreja, K.N.1    Yin, A.2    Martino, I.3
  • 30
    • 0014776873 scopus 로고
    • River Flow Forecasting Through Conceptual Models, Part 1 - A Discussion of Principles
    • Nash, J.E. J.V. Sutcliffe, 1970. River Flow Forecasting Through Conceptual Models, Part 1 - A Discussion of Principles. Journal of Hydrology 10 (3 282 290.
    • (1970) Journal of Hydrology , vol.10 , Issue.3 , pp. 282-290
    • Nash, J.E.1    Sutcliffe, J.V.2
  • 32
    • 0027464578 scopus 로고
    • Neural Network Screening for Groundwater Reclamation under Uncertainty
    • Ranjithan, S., J.W. Eheart J.H. Rasset, Jr., 1993. Neural Network Screening for Groundwater Reclamation under Uncertainty. Water Resources Research 29 (3 563 574.
    • (1993) Water Resources Research , vol.29 , Issue.3 , pp. 563-574
    • Ranjithan, S.1    Eheart, J.W.2    Rasset Jr., J.H.3
  • 34
    • 0030272354 scopus 로고    scopus 로고
    • Chaotic Recurrent Neural Networks and Their Application to Speech Recognition
    • Ryeu, J.K. H.S. Chung, 1996. Chaotic Recurrent Neural Networks and Their Application to Speech Recognition. Journal of Neurocomputing 13 : 281 294.
    • (1996) Journal of Neurocomputing , vol.13 , pp. 281-294
    • Ryeu, J.K.1    Chung, H.S.2
  • 35
    • 1642434728 scopus 로고    scopus 로고
    • Spatial-Temporal Drought Analysis of South Korea based on Neural Networks
    • [In Korean]
    • Shin, H.S. M.J. Park, 1999. Spatial-Temporal Drought Analysis of South Korea based on Neural Networks. Journal of Korea Water Resources Association 32 (1 15 29. [In Korean]
    • (1999) Journal of Korea Water Resources Association , vol.32 , Issue.1 , pp. 15-29
    • Shin, H.S.1    Park, M.J.2
  • 38
    • 0000859675 scopus 로고
    • Cross Validation Choice and Assessment of Statistical Predictions
    • Stone, M., 1974. Cross Validation Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society B 36 : 44 47.
    • (1974) Journal of the Royal Statistical Society B , vol.36 , pp. 44-47
    • Stone, M.1
  • 40
  • 42
    • 2142811173 scopus 로고    scopus 로고
    • Uncertainty of Predictions of Embankment Dam Breach Parameters
    • Wahl, T.L., 2004. Uncertainty of Predictions of Embankment Dam Breach Parameters. Journal of Hydraulic Engineering, ASCE 130 (5 389 397.
    • (2004) Journal of Hydraulic Engineering, ASCE , vol.130 , Issue.5 , pp. 389-397
    • Wahl, T.L.1
  • 45
    • 16444377510 scopus 로고    scopus 로고
    • Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty
    • Zou, R., W.S. Lung H. Guo, 2002. Neural Network Embedded Monte Carlo Approach for Water Quality Modeling under Input Information Uncertainty. Journal of Computing in Civil Engineering, ASCE 16 (2 135 142.
    • (2002) Journal of Computing in Civil Engineering, ASCE , vol.16 , Issue.2 , pp. 135-142
    • Zou, R.1    Lung, W.S.2    Guo, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.