참고문헌
- 안중배, 류정희, 조익현, 박주영, 류상범 (1997) '한반도 기온 및 강수량과 주변 해역 해면온도와의 상관관계에 관한 연구'., 한국기상학회 논문집, Vol. 33, No. 2, pp. 128-141
- 오재호 (1999). 기후와 대기순환, 아르케
- 오재호 (1999). 변화하는 기후, 아르케
- 윤용남 (1998). 공업수문학, 청문각
- Bodri, L. and Cermak, V. )2000). 'Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia', Advances in Engineering Software, Vol. 31, p. 312-321 https://doi.org/10.1016/S0965-9978(99)00063-0
- Chang, F.J. and Chen, Y.C. (2001). 'A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction', Journal of Hydrology, Vol. 245, p. 153-164 https://doi.org/10.1016/S0022-1694(01)00350-X
- Eltahir, E.A.B. (1998a). 'A Soil moisture-rainfall feedback mechanics : Theory and observation', Water Resources Research, Vol. 34, No. 4, p. 756-776
- Eltahir, E.A.B. (1998b). 'A Soil moisture-rainfall feedback mechanics : Numerical experiment', Water Resources Research, Vol. 34, No. 4, p. 777-785 https://doi.org/10.1029/97WR03497
- Franks, S.W., Gineste, P., Beven, K.J., and Merot, P. (1998) 'On constraining the predictions of a distributed model : The incorporation of fuzzy estimates of saturated areas into the calibration process', Water Resources Research, Vol. 34, No. 4, p. 787-797 https://doi.org/10.1029/97WR03041
- Furundzic, D. (1998). 'Application example of neural networks for time series analysis', Signal Process, Vol. 64, p. 383-396 https://doi.org/10.1016/S0165-1684(97)00203-X
- Gautam, D.K. and Holz, K.P. (2001). 'Rainfall-runoff modelling using adaptive neuro-fuzzy systems', Jounal of Hydroinfomatics, March, p. 3-10
- Jang, J.S.R., Sun, C.T., and Mizutani, E. (1996). Neuro-fuzzy and soft computing : A Computational Approach to Learning and Machin Intelligence, Prentice Hall
- Kang, In-Sik, Hee-jung Baek (1993). 'Long range prediction of winter monthly-mean temperature in Korea', J. Kor. Meteo. Soo., Vol. 30, p. 247-260
- Lange, N.T. (1998). 'New Mathmatical Approach Hydrological modeling-An Application of Aricficial Neural Network', Phys. Chem. Earth, Vol. 24, No. 1-2, p. 31-35
- Lin, C.T. and C.S.G. Lee (1999). Neural Fuzzy Systems : A Neuro-Fuzzy Synergism to Intelligent Systems, Prentice Hall
- Luk, K.C., Ball, J.E., Sharma, A. (2001). 'An application of artificial neural networks for rainfall forecasting', Mathmatical and Computer Modeling, Vol. 33, p. 683-693 https://doi.org/10.1016/S0895-7177(00)00272-7
- Ouenes, A. (2000). 'Practical application of fuzzy logic and neural networks to fractured reservoir characterization', Computers Geosciences, Vol. 26, p. 953-962 https://doi.org/10.1016/S0098-3004(00)00031-5
- Ozelkan, E.C. (1996). 'Relationship between monthly atmospheric circulation patterns and precipitation : Fuzzy logic and regression approaches', Water Resources Research, Vol. 32, No. 7, p. 2097-2103 https://doi.org/10.1029/96WR00289
- Sajikumar, N. and Thandaveswara, B.S. (1999). 'A non-linear model using an artificial neural network', Journal of Hydrology, Vol. 216, p. 32-55 https://doi.org/10.1016/S0022-1694(98)00273-X
- Valdes, J. B., Entekhabi, D., and Bartolini, P. (1994). 'Long term predictability of river stages under ENSO influence, ASCE Hydraulic Engineering', Vol. 8, No. 1, p. 366-370
- Waylen, P. R. and Caviedes, C. N. (1990). 'Annual and seasonal fluctuations of precipitation and streamflow in the Aconcagua River Basin', Journal of Hydrology, Vol. 120, p. 79-102 https://doi.org/10.1016/0022-1694(90)90143-L
- Woolhiser, D. A. and Keefer, T. O. (1993). 'Southern Oscillation effects on daily precipitation in the southwestern United States', Water Resources Research, Vol. 29, No. 4, p. 1287-1295 https://doi.org/10.1029/92WR02536