메뉴 건너뛰기




Volumn 36, Issue 1, 2009, Pages 29-37

Artificial neural network for tsunami forecasting

Author keywords

Artificial neural network; Data driven model; Tsunami forecast

Indexed keywords

ARRIVAL TIME; ARTIFICIAL NEURAL NETWORK; DATA SET; EARTHQUAKE RUPTURE; FORECASTING METHOD; TSUNAMI; WAVE GENERATION; WAVE HEIGHT;

EID: 67349155562     PISSN: 13679120     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jseaes.2008.11.003     Document Type: Article
Times cited : (33)

References (34)
  • 1
    • 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. Journal of Hydrologic Engineering ASCE 5 2 (2000) 115-123
    • (2000) Journal of Hydrologic Engineering ASCE , vol.5 , Issue.2 , pp. 115-123
  • 2
    • 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. Journal of Hydrologic Engineering ASCE 5 2 (2000) 124-137
    • (2000) Journal of Hydrologic Engineering ASCE , vol.5 , Issue.2 , pp. 124-137
  • 3
    • 33845616686 scopus 로고    scopus 로고
    • Tsunami travel time prediction using neural networks
    • doi:10.1029/2006GL026688
    • Barman, R., Kumar, B.P., Pandey, P.C., and Dube, S.K., 2006. Tsunami travel time prediction using neural networks. Geophysical Research Letters, 33(16), L16612, doi:10.1029/2006GL026688.
    • (2006) Geophysical Research Letters , vol.33 , Issue.16
    • Barman, R.1    Kumar, B.P.2    Pandey, P.C.3    Dube, S.K.4
  • 6
    • 0032961025 scopus 로고    scopus 로고
    • River flood forecasting with a neural network model
    • Campolo M., Andreussi P., and Soldati A. River flood forecasting with a neural network model. Water Resource Research 35 (1999) 1191-1197
    • (1999) Water Resource Research , vol.35 , pp. 1191-1197
    • Campolo, M.1    Andreussi, P.2    Soldati, A.3
  • 8
    • 51449094220 scopus 로고    scopus 로고
    • Tsunami forecasting using proper orthogonal decomposition method
    • doi:10.1029/2007JC004583
    • Dao, M.H., Tkalich, P., and Chan, E.S., 2008. Tsunami forecasting using proper orthogonal decomposition method. Journal of Geophysical Research 113, C06019, doi:10.1029/2007JC004583.
    • (2008) Journal of Geophysical Research , vol.113
    • Dao, M.H.1    Tkalich, P.2    Chan, E.S.3
  • 11
    • 0345292373 scopus 로고    scopus 로고
    • Numerical Method of Tsunami Simulation with the Leap-Frog Scheme (IUGG/IOC Time Project)
    • UNESCO
    • Goto, C., Ogawa, Y., Shuto, N., and Imamura, F., 1997. Numerical Method of Tsunami Simulation with the Leap-Frog Scheme (IUGG/IOC Time Project). IOC Manual, UNESCO, No. 35.
    • (1997) IOC Manual , Issue.35
    • Goto, C.1    Ogawa, Y.2    Shuto, N.3    Imamura, F.4
  • 13
    • 0024880831 scopus 로고
    • Multilayer feedfoward networks are universal approximators
    • Hornick K., Maxwell S., and Halbert W. Multilayer feedfoward networks are universal approximators. Neural Networks 2 (1989) 359-366
    • (1989) Neural Networks , vol.2 , pp. 359-366
    • Hornick, K.1    Maxwell, S.2    Halbert, W.3
  • 14
    • 0029413797 scopus 로고
    • Artificial neural networks modeling of the rainfall-runoff process
    • Hsu K., Gupta H.V., and Sorooshian S. Artificial neural networks modeling of the rainfall-runoff process. Water Resources Research 31 10 (1995) 2517-2530
    • (1995) Water Resources Research , vol.31 , Issue.10 , pp. 2517-2530
    • Hsu, K.1    Gupta, H.V.2    Sorooshian, S.3
  • 15
    • 0035472003 scopus 로고    scopus 로고
    • River flow time series prediction with a range dependent neural network
    • Hu T.S., Lam K.C., and NG S.T. River flow time series prediction with a range dependent neural network. Hydrol Sci. J. 46 5 (2001) 729-745
    • (2001) Hydrol Sci. J. , vol.46 , Issue.5 , pp. 729-745
    • Hu, T.S.1    Lam, K.C.2    NG, S.T.3
  • 17
    • 11144285078 scopus 로고    scopus 로고
    • Prediction of water level in a river mouth using neural network approach
    • Lee, H.S. and Tanaka, H., 2002. Prediction of water level in a river mouth using neural network approach. In: Proceedings of the 13th IAHR-APD, 669-674.
    • (2002) Proceedings of the 13th IAHR-APD , pp. 669-674
    • Lee, H.S.1    Tanaka, H.2
  • 19
    • 0033957764 scopus 로고    scopus 로고
    • Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications
    • Maier H.R., and Dandy G.C. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling and Software 15 (2000) 101-124
    • (2000) Environmental Modelling and Software , vol.15 , pp. 101-124
    • Maier, H.R.1    Dandy, G.C.2
  • 21
    • 67349232181 scopus 로고    scopus 로고
    • Matlab: The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, United States (Tax ID# 942960235; Phone: 508 647 7000; Fax: 508 647 7001).
    • Matlab: The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, United States (Tax ID# 942960235; Phone: 508 647 7000; Fax: 508 647 7001).
  • 24
    • 67349161214 scopus 로고    scopus 로고
    • Srivichai, M., Supharatid, S., and Imamura, F., 2006. Developing of forecasted tsunami database along Thailand Andaman coastline. In: Proceeding, Asia Oceania Geosciences Society 3rd Annual Meeting, AOGS2006, p. 138.
    • Srivichai, M., Supharatid, S., and Imamura, F., 2006. Developing of forecasted tsunami database along Thailand Andaman coastline. In: Proceeding, Asia Oceania Geosciences Society 3rd Annual Meeting, AOGS2006, p. 138.
  • 25
    • 0037359617 scopus 로고    scopus 로고
    • Tidal level forecasting and filtering by neural network model
    • Supharatid S. Tidal level forecasting and filtering by neural network model. Coastal Engineering Journal 45 1 (2003) 119-138
    • (2003) Coastal Engineering Journal , vol.45 , Issue.1 , pp. 119-138
    • Supharatid, S.1
  • 27
    • 51449103863 scopus 로고    scopus 로고
    • Tsunami propagation modelling and forecasting for early warning system
    • Tkalich P., Dao M.H., and Chan E.S. Tsunami propagation modelling and forecasting for early warning system. Journal of Earthquake and Tsunami 1 1 (2007) 87-98
    • (2007) Journal of Earthquake and Tsunami , vol.1 , Issue.1 , pp. 87-98
    • Tkalich, P.1    Dao, M.H.2    Chan, E.S.3
  • 28
    • 58149260400 scopus 로고    scopus 로고
    • Tolkova, E., 2008. Principal component analysis of tsunami buoy record: Tide prediction and removal. Dyn. Atmos. Oceans, doi:10.1016/j.dynatmoce.2008.03.001.
    • Tolkova, E., 2008. Principal component analysis of tsunami buoy record: Tide prediction and removal. Dyn. Atmos. Oceans, doi:10.1016/j.dynatmoce.2008.03.001.
  • 34
    • 0012454791 scopus 로고    scopus 로고
    • Predicting tsunami heights along the North American coast from tsunamis generated in the Northwest Pacific Ocean during tsunami warnings
    • Whitmore P.M., and Sokolowski T.J. Predicting tsunami heights along the North American coast from tsunamis generated in the Northwest Pacific Ocean during tsunami warnings. Science of Tsunami Hazards 14 3 (1996) 147-166
    • (1996) Science of Tsunami Hazards , vol.14 , Issue.3 , pp. 147-166
    • Whitmore, P.M.1    Sokolowski, T.J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.