메뉴 건너뛰기




Volumn 17, Issue 1, 2012, Pages 89-97

Comparison of machine learning methods for runoff forecasting in mountainous watersheds with limited data

Author keywords

Himalayas; Machine learning; Runoff forecasting; Support vector regression; Wavelet networks

Indexed keywords


EID: 84878346183     PISSN: 14297426     EISSN: 20834535     Source Type: Journal    
DOI: 10.2478/v10025-012-0038-4     Document Type: Article
Times cited : (17)

References (29)
  • 1
    • 80052027629 scopus 로고    scopus 로고
    • A wavelet neural network conjunction model for groundwater level forecasting
    • ADAMOWSKI J., CHAN H.F. 2011. A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology. No 407 p. 28-40.
    • (2011) Journal of Hydrology , Issue.407 , pp. 28-40
    • Adamowski, J.1    Chan, H.F.2
  • 2
    • 84865047835 scopus 로고    scopus 로고
    • Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for rainfall-runoff forecasting in Himalayan micro watersheds with limited data
    • ADAMOWSKI J., CHAN H.F., PRASHER S.O., SHARDA V.N. 2011. Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for rainfall-runoff forecasting in Himalayan micro watersheds with limited data. Journal of Hydroinformatics. No 3 p. 731-744.
    • (2011) Journal of Hydroinformatics , Issue.3 , pp. 731-744
    • Adamowski, J.1    Chan, H.F.2    Prasher, S.O.3    Sharda, V.N.4
  • 3
    • 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. Journal of Hydrology. No 15 p. 729-743.
    • (2010) Journal of Hydrology , Issue.15 , pp. 729-743
    • Adamowski, J.1    Karapataki, C.2
  • 4
    • 77955276087 scopus 로고    scopus 로고
    • Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds
    • 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. Journal of Hydrology. No 390 p. 85-91.
    • (2010) Journal of Hydrology , Issue.390 , pp. 85-91
    • Adamowski, J.1    Sun, K.2
  • 5
    • 31044438334 scopus 로고    scopus 로고
    • Multi-time scale stream flow predictions: The support vector machines approach
    • ASEFA T., KEMBLOWSKI M., MCKEE M., KHALIL A. 2005. Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology. No 318 p. 7-16.
    • (2005) Journal of Hydrology , Issue.318 , pp. 7-16
    • Asefa, T.1    Kemblowski, M.2    McKee, M.3    Khalil, A.4
  • 6
    • 60249084777 scopus 로고    scopus 로고
    • Generalization performance of support vector machines and neural networks in runoff modeling
    • BEHZAD M., ASGHARI K., EAZI M., PALHANG M. 2009. Generalization performance of support vector machines and neural networks in runoff modeling. Expert Systems with Applications. No 36 p. 7624-7629.
    • (2009) Expert Systems with Applications , Issue.36 , pp. 7624-7629
    • Behzad, M.1    Asghari, K.2    Eazi, M.3    Palhang, M.4
  • 7
    • 33751401308 scopus 로고    scopus 로고
    • Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning
    • CANNAS B., FANNI A., SIAS G., SEE L. 2006. Data preprocessing for river flow forecasting using neural networks: Wavelet transforms and data partitioning. Physics and Chemistry of the Earth. No 31 p. 1164-1171.
    • (2006) Physics and Chemistry of the Earth , Issue.31 , pp. 1164-1171
    • Cannas, B.1    Fanni, A.2    Sias, G.3    See, L.4
  • 11
    • 0003425664 scopus 로고    scopus 로고
    • Technical report. Southampton. University of Southampton. Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science
    • GUNN S. 1998. Support vector machines for classification and regression. Technical report. Southampton. University of Southampton. Faculty of Engineering, Science and Mathematics, School of Electronics and Computer Science pp.54.
    • (1998) Support Vector Machines for Classification and Regression , pp. 54
    • Gunn, S.1
  • 13
    • 68049112473 scopus 로고    scopus 로고
    • Neural networks and wavelet conjunction model for intermittent streamflow forecasting
    • KISI O. 2009. Neural networks and wavelet conjunction model for intermittent streamflow forecasting. Journal of Hydrologic Engineering. No 14 p. 773-782.
    • (2009) Journal of Hydrologic Engineering , Issue.14 , pp. 773-782
    • Kisi, O.1
  • 16
    • 33645973241 scopus 로고    scopus 로고
    • Monthly runoff simulation: Comparing and combining conceptual and neural and network models
    • NILSSON P., UVO C., BERNTDSSON R. 2005. Monthly runoff simulation: Comparing and combining conceptual and neural and network models. Journal of Hydrology. No 321 p. 344-363.
    • (2005) Journal of Hydrology , Issue.321 , pp. 344-363
    • Nilsson, P.1    Uvo, C.2    Berntdsson, R.3
  • 17
    • 77953122265 scopus 로고    scopus 로고
    • Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model
    • PANDA R.K., PRAMANIK N., BALA B. 2009. Simulation of river stage using artificial neural network and MIKE 11 hydrodynamic model. Computers and Geosciences. No 36 p. 735-745.
    • (2009) Computers and Geosciences , Issue.36 , pp. 735-745
    • Panda, R.K.1    Pramanik, N.2    Bala, B.3
  • 18
    • 65249094814 scopus 로고    scopus 로고
    • River flow forecasting using different artificial neural network algorithms and wavelet transform
    • PARTAL T. 2009. River flow forecasting using different artificial neural network algorithms and wavelet transform. Canadian Journal of Civil Engineering. No 36 p. 26-38.
    • (2009) Canadian Journal of Civil Engineering , Issue.36 , pp. 26-38
    • Partal, T.1
  • 19
    • 34447527322 scopus 로고    scopus 로고
    • Wavelet and neuro-fuzzy conjunction model for precipitation forecasting
    • PARTAL T., KISI O. 2008. Wavelet and neuro-fuzzy conjunction model for precipitation forecasting. Journal of Hydrology. No 342 p. 199-212.
    • (2008) Journal of Hydrology , Issue.342 , pp. 199-212
    • Partal, T.1    Kisi, O.2
  • 20
    • 69949143939 scopus 로고    scopus 로고
    • Application of neural network and adaptive neuro fuzzy inference systems for stream flow prediction
    • PRAMANIK N., PANDA R.K. 2009. Application of neural network and adaptive neuro fuzzy inference systems for stream flow prediction. Hydrological Science Journal. No 54 p. 247-260.
    • (2009) Hydrological Science Journal , Issue.54 , pp. 247-260
    • Pramanik, N.1    Panda, R.K.2
  • 21
    • 70449453571 scopus 로고    scopus 로고
    • Comparative study of some improved ANN models for hydrologic time series forecast
    • May 19-21, 2009. Xiamen, China. Los Alamitos. IEEE Computer Society
    • SANG Y., WANG D., WU J. 2009. Comparative study of some improved ANN models for hydrologic time series forecast. Intelligent Systems, GCIS '09, WRI Global Congress. May 19-21, 2009. Xiamen, China. Los Alamitos. IEEE Computer Society. Vol. 4 p. 63-67.
    • (2009) Intelligent Systems, GCIS '09, WRI Global Congress , vol.4 , pp. 63-67
    • Sang, Y.1    Wang, D.2    Wu, J.3
  • 22
    • 75149184952 scopus 로고    scopus 로고
    • Artificial neural network model for river flow forecasting in a developing country
    • SHAMSELDIN A.Y. 2010. Artificial neural network model for river flow forecasting in a developing country. Journal of Hydroinformatics. No 12 p. 22-35.
    • (2010) Journal of Hydroinformatics , Issue.12 , pp. 22-35
    • Shamseldin, A.Y.1
  • 23
    • 33747356756 scopus 로고    scopus 로고
    • Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques
    • SHARDA V., PATEL R.M., PRASHER S.O., OJASVI P.R., PRAKASH C. 2006. Modeling runoff from middle Himalayan watersheds employing artificial intelligence techniques. Agriculture Water Management. No 83 p. 233-242.
    • (2006) Agriculture Water Management , Issue.83 , pp. 233-242
    • Sharda, V.1    Patel, R.M.2    Prasher, S.O.3    Ojasvi, P.R.4    Prakash, C.5
  • 25
    • 79959790257 scopus 로고    scopus 로고
    • A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting
    • TIWARI M.K., CHATTERJEE C. 2010. A new wavelet-bootstrap-ANN hybrid model for daily discharge forecasting. Journal of Hydroinformatics. No 13 p. 500-519.
    • (2010) Journal of Hydroinformatics , Issue.13 , pp. 500-519
    • Tiwari, M.K.1    Chatterjee, C.2
  • 26
    • 0003450542 scopus 로고
    • The nature of statistical learning theory
    • New York. Springer Verl. ISBN 978-0-387-98780-4
    • VAPNIK V. 1995. The nature of statistical learning theory. Ser. Information Science and Statistics. New York. Springer Verl. ISBN 978-0-387-98780-4 pp. 314.
    • (1995) Ser. Information Science and Statistics , pp. 314
    • Vapnik, V.1
  • 27
    • 68349105875 scopus 로고    scopus 로고
    • A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series
    • WANG W., CHAU K.C., CHENG L.Q. 2009. A comparison of performance of several artificial intelligence methods for forecasting monthly discharge time series. Journal of Hydrology. No 374 p. 294-306.
    • (2009) Journal of Hydrology , Issue.374 , pp. 294-306
    • Wang, W.1    Chau, K.C.2    Cheng, L.Q.3
  • 28
    • 65749118118 scopus 로고    scopus 로고
    • Methods to improve neural network performance in daily flows prediction
    • WU C., CHAU K., LI Y. 2009. Methods to improve neural network performance in daily flows prediction. Journal of Hydrology. No 372 p. 80-93.
    • (2009) Journal of Hydrology , Issue.372 , pp. 80-93
    • Wu, C.1    Chau, K.2    Li, Y.3
  • 29
    • 0033019602 scopus 로고    scopus 로고
    • Short term streamflow forecasting using artificial neural networks
    • ZEALAND C., BURN D.H., SIMONOVIC S.P. 1998. Short term streamflow forecasting using artificial neural networks, Journal of Hydrology. No 214 p. 32-48.
    • (1998) Journal of Hydrology , Issue.214 , pp. 32-48
    • Zealand, C.1    Burn, D.H.2    Simonovic, S.P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.