최소 단어 이상 선택하여야 합니다.
최대 10 단어까지만 선택 가능합니다.
다음과 같은 기능을 한번의 로그인으로 사용 할 수 있습니다.
NTIS 바로가기한국농공학회지 = Journal of the Korean Society of Agricultural Engineers, v.45 no.2, 2003년, pp.45 - 57
강문성 (서울대학교 농업생명과학연구원) , 박승우 (서울대학교 농공대학)
An artificial neural network model was developed to analyze and forecast Short-term river runoff from the Naju watershed, in Korea. Error back propagation neural networks (EBPN) of hourly rainfall and runoff data were found to have a high performance In forecasting runoff. The number of hidden nodes...
Ahn, K. S., and J. H. Kim. 1998. A study on the simulation of runoff hydrograph by using artificial neural network. Journal of Korea Water Resources Association 31(1): 13-25. (in Korean)
Anmala. J., and K. V. Nedunuri. 1995. Neural networks for prediction of watershed runoff, 1st International Conference on Water Resources Eng. Water Resources Planning and Management Div., ASCE, San Antonio, Texas, USA, Aug. 14-18, pp. 348-357
Anthony, W. M., 1996. Extened rainfall-runoff nodeling using aritificial neural networks, Proc. of the 2nd International Conf. on Hydroinforrnatics, '96, Zurich, Switzerland, pp, 207-213
Asaad, Y. S.. 1997. Application of a neural network technique to rainfall-runoff modeling. Journal of Hydrology 199: 272-294
Choi, J. K., and M. S. Kang. 2000. Application of neural network to water resources. Korean National Committee on Irrigation and Drainage Journal 7(2): 246-258. (in Korean)
Dawson, C. W., and R. Wilby. 1998. An artificial neural network approach to rainfall-runoff modeling, Hydrological Sciences 43(1): 47-66
Hsu, K. N., H. V. Gupta, and S. sorooshian. 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research 31(10): 2517-2530
Huynh, N. P. and S. Sureerattanan, 2000. Neural networks for filtering and forecasting of daily and monthly streamflows, Water Resources Publications, LLC, WEESHE, Hydrologic Modeling, pp. 203-218
Kang, M. S., and S. W. Park. 2001. Forecasting long-term streamflow from a small watershed using artificial neural network. Journal of the Korean Society of Agricultural Engineers 43(2): 69-77. (in Korean)
Kim, J. H. 1993. A study on hydrologic forecasting of streamflows based on artificial neural network. Ph.D. diss. Inha University. (in Korean)
Marina, C., P. Andreussi, and A. Soldati. 1999. River flood forecasting with a neural network model. Water Resources Research 35(4): 1191-1197
Nash. J. E. and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models part I-A discussion of principles. Journal of Hydrology 10: 282-290
Sajikumar, N. and B. S. Thandaveswara. 1999. A nonlinear rainfall-runoff model using ANN. Journal of Hydrology, 216: 32-55
Shim, S. B., M. S. Kim, and K. C. Shim. 1998. Flood inflow forecasting on multipurpose reservoir by neural network. Journal of Korea Water Resources Association 31 (1): 45-57. (in Korean)
Shin. H. S., and M. J. Park. 1999. Spatial analysis for mean annual precipitation based on neural networks. Journal of Korea Water Resources Association 32(1): 3-13. (in Korean)
Shin, H. S.. and M. J. Park. 1999. Spatialtemporal drought analysis of South Korea based on neural networks. Journal of Korea Water Resources Association 32(1): 15-29. (in Korean)
Shin, H. S.. 1998. Application of neural network to water resources and environments. Magazine of Korea Water Resources Association 31 (1): 97 -103. (in Korean)
Sureerattanar, S. and H. N. Phien. 1997. Back-propagation networks for daily stream flow forecasting. Water Resources Journal No. 195: 1-7
Zealand, C. M., D. H. Burn, and S. P. Simonovic. 1999. Short term stream flow forecasting using ANN, Journal of Hydrology, Vol., 214, pp. 32-48
해당 논문의 주제분야에서 활용도가 높은 상위 5개 콘텐츠를 보여줍니다.
더보기 버튼을 클릭하시면 더 많은 관련자료를 살펴볼 수 있습니다.
*원문 PDF 파일 및 링크정보가 존재하지 않을 경우 KISTI DDS 시스템에서 제공하는 원문복사서비스를 사용할 수 있습니다.
※ AI-Helper는 부적절한 답변을 할 수 있습니다.