المؤلفون: 江衍銘, 張斐章, Chiang, Yen-Ming, Chang, Fi-John

مصطلحات موضوعية: 氣象雷達, 類神經網路, 定量降雨預報模式, 串列式架構, 多階段洪水預測模式, Meteorological radar, Artificial neural network, Quantitative precipitation forecasting, Recursive structure, Multi-step-ahead flood forecasting

Relation: 台灣水利 55 (2): 25-33; http://ntur.lib.ntu.edu.tw/handle/246246/176321; http://ntur.lib.ntu.edu.tw/bitstream/246246/176321/1/以類神經網路建構定量降雨及多階段.pdf

الاتاحة: http://ntur.lib.ntu.edu.tw/handle/246246/176321
http://ntur.lib.ntu.edu.tw/bitstream/246246/176321/1/以類神經網路建構定量降雨及多階段.pdf

المؤلفون: 陳思瑋, Chen, Szu-Wei

المساهمون: 臺灣大學: 土木工程學研究所, 李天浩

مصطلحات موضوعية: 地形, 颱風, 數值天氣預報, 定量降雨預報, Terrain, Typhoon, Numerical Weather Prediction, Quantitative Precipitation Nowcast

وصف الملف: 12507230 bytes; application/pdf

Relation: http://ntur.lib.ntu.edu.tw/handle/246246/255528; http://ntur.lib.ntu.edu.tw/bitstream/246246/255528/1/ntu-100-R98521319-1.pdf

الاتاحة: http://ntur.lib.ntu.edu.tw/handle/246246/255528
http://ntur.lib.ntu.edu.tw/bitstream/246246/255528/1/ntu-100-R98521319-1.pdf

المؤلفون: 林志鴻, Lin, Chih-Hung

المساهمون: 陳文福, 中興大學, 周天穎, 謝平城

مصطلحات موضوعية: 洪氾區, floodplain, 截彎取直, 非工程方法, 定量降雨預報, 防洪社區, 集水區, 區域排水, SOBEK二維淹水模式, 重現期距, nonstructural measure, quantity rainfall predict, disaster resilient communities, watershed, regional drainage, SOBEK 2-D flooding simulation model, recurrence interval, envir, geo

Relation: http://hdl.handle.net/11455/34836

الاتاحة: http://hdl.handle.net/11455/34836

المؤلفون: 龔楚, Kung, Chu-Ying

المساهمون: 臺灣大學: 大氣科學研究所, 李清勝, 王重傑

مصطلحات موضوعية: 梅雨鋒面, 氣候概念模式, 定量降雨預報, 強降雨, 綜觀環境, 豪大雨預報檢查表, Mei-yu front, conceptual climatology model, quantitative precipitation forecast, heavy rain, synoptic environment, heavy rainfall checklist, geo, envir

Relation: http://ntur.lib.ntu.edu.tw/bitstream/246246/248772/1/ntu-99-R97229008-1.pdf; http://ntur.lib.ntu.edu.tw/handle/246246/248772

الاتاحة: http://ntur.lib.ntu.edu.tw/bitstream/246246/248772/1/ntu-99-R97229008-1.pdf
http://ntur.lib.ntu.edu.tw/handle/246246/248772

المؤلفون: 龔楚, Kung, Chu-Ying

المساهمون: 臺灣大學: 大氣科學研究所, 李清勝, 王重傑

مصطلحات موضوعية: 梅雨鋒面, 氣候概念模式, 定量降雨預報, 強降雨, 綜觀環境, 豪大雨預報檢查表, Mei-yu front, conceptual climatology model, quantitative precipitation forecast, heavy rain, synoptic environment, heavy rainfall checklist

وصف الملف: 3458573 bytes; application/pdf

Relation: http://ntur.lib.ntu.edu.tw/handle/246246/248772; http://ntur.lib.ntu.edu.tw/bitstream/246246/248772/1/ntu-99-R97229008-1.pdf

الاتاحة: http://ntur.lib.ntu.edu.tw/handle/246246/248772
http://ntur.lib.ntu.edu.tw/bitstream/246246/248772/1/ntu-99-R97229008-1.pdf

المؤلفون: 林志鴻, Lin, Chih-Hung

المساهمون: 陳文福, 中興大學, 周天穎, 謝平城

مصطلحات موضوعية: 洪氾區, floodplain, 截彎取直, 非工程方法, 定量降雨預報, 防洪社區, 集水區, 區域排水, SOBEK二維淹水模式, 重現期距, nonstructural measure, quantity rainfall predict, disaster resilient communities, watershed, regional drainage, SOBEK 2-D flooding simulation model, recurrence interval

Relation: U0005-2306200919541900; http://hdl.handle.net/11455/34836

الاتاحة: http://hdl.handle.net/11455/34836

المؤلفون: 林志鴻, Lin, Chih-Hung

المساهمون: 陳文福, 中興大學

مصطلحات موضوعية: 洪氾區, floodplain, 截彎取直, 非工程方法, 定量降雨預報, 防洪社區, 集水區, 區域排水, SOBEK二維淹水模式, 重現期距, nonstructural measure, quantity rainfall predict, disaster resilient communities, watershed, regional drainage, SOBEK 2-D flooding simulation model, recurrence interval, envir, geo

Relation: http://hdl.handle.net/11455/33883

الاتاحة: http://hdl.handle.net/11455/33883

المؤلفون: 林志鴻, Lin, Chih-Hung

المساهمون: 陳文福, 中興大學

مصطلحات موضوعية: 洪氾區, floodplain, 截彎取直, 非工程方法, 定量降雨預報, 防洪社區, 集水區, 區域排水, SOBEK二維淹水模式, 重現期距, nonstructural measure, quantity rainfall predict, disaster resilient communities, watershed, regional drainage, SOBEK 2-D flooding simulation model, recurrence interval

Relation: http://hdl.handle.net/11455/33883

الاتاحة: http://hdl.handle.net/11455/33883

المؤلفون: 江衍銘, Chiang, Yen-Ming

المساهمون: 張斐章, 臺灣大學：生物環境系統工程學研究所

مصطلحات موضوعية: 類神經網路, 多階段洪水預測, 雷達, 數值天氣預報, 序列式傳遞架構, 定量降雨預報, 融合程序, artificial neural network, multi-step-ahead flood forecasting, radar, numerical weather prediction, serial-propagated structure, quantitative precipitation forecasting, merging procedure

وصف الملف: 2064020 bytes; application/pdf

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El-Shoura, S.I. Shaheen, and M.S. El-Sherif, 1999. A comparison between neural-network forecasting techniques－case study: river flow forecasting. IEEE Trans. on Neural Networks 10(2): 402-409. Atlas, D., R.C. Srivastava, and R.S. Sekhon, 1973. Doppler radar characteristics of precipitation at vertical incidence. Review of Geophysics 11: 1-35. Baltas, E. and M. Mimikou, 1994. Short-term rainfall forecasting using radar data. Water Resources Development 10: 67-78. Baratti, R., B. Cannas, A. Fanni, M. Pintus, G. M. Sechi, and N. Toreno, 2003. River flow forecast for reservoir management through neural networks. Neurocomputing 55(3-4): 421-437. Chang, F.J., L.C. Chang, and H.L. Huang, 2002. Real-time recurrent learning neural network for stream-flow forecasting. Hydrological Processes 16(13): 2577-2588. Chang, F.J. and Y.C. Chen, 2001. A counterpropagation fuzzy-neural network modeling approach to real time stream flow prediction. Journal of Hydrology 245: 153-164. Chang, F.J. and Y.C. 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Principles of neurocomputing for science & engineering. McGraw-Hill. New York. Han, M., J.H. Xi, S.G. Xu, and F.L. Yin, 2004. Prediction of chaotic time series based on the recurrent predictor neural network. IEEE Transactions on Signal Processing 52: 3409-3416. Haykin, S., 1999. Neural Network: A comprehensive foundation. 2nd ed. Prentice Hall. Hong, Y., Y.M. Chiang, Y. Liu, K.L. Hsu, and S. Sorooshian, 2006. Satellite-based precipitation estimation using watershed segmentation and growing hierarchical self-organizing map. International Journal of Remote Sensing (in press) Hopfield, J.J., 1982. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79(8): 2254-2258. Hsu, K.L., H.V. Gupta, and S. Sorooshian, 1995. Artificial neural network modeling of the rainfall-runoff process. Water Resources Research 31: 2517-2530. Hsu, K.L., H.V. Gupta, X.G. Gao, and S. Sorooshian, 1999. Estimation of physical variables from multichannel remotely sensed imagery using a neural network: Application to rainfall estimation. Water Resources Research 35(5): 1605-1618. Hsu, K.L., H.V. Gupta, X.G. Gao, S. Sorooshian, and B. Imam, 2002. Self-organizing linear output (SOLO): An artificial neural network suitable for hydrologic modeling and analysis. Water Resources Research 38(12): 1302, doi:10.1029/ 2001WR000795. Hsu, K. L., X. Gao, S. Sorooshian, and H. V. Gupta, 1997: Precipitation estimation from remotely sensed information using artificial neural networks. Journal of Applied Meteorology, 36: 1176-1190. Huang, S.J. and R.J. Lian, 2000. A combination of fuzzy logic and neural network controller for multiple-input multiple-output systems. International Journal of Systems Science 31: 343-357. Hydrological Engineering Center, 1990. HEC-1 Flood Hydrograph Package. Program Users Manual. U. S. Army Crops of Engineers: Davis, C.A. Imrie, C.E., S. Durucan, and A. Korre, 2000. River flow prediction using artificial neural network: generalisation beyond the calibration range. Journal of Hydrology 233: 138-153. Johnson, J.T., P.L. MacKeen, A. Witt, E.D. Mitchell, G.J. Stumpf, M.D. Eilts, and K.W. Thomas, 1993. The storm cell identificationand tracking algorithm: an enhanced WSR-88D algorithm. Weather and Forecasting 13: 263-276. Jorgensen, D.P. and P.T. Willis, 1982. A Z-R relationship for hurricianes. Journal of Applied Meteorology 21: 356-366. Karunanithi, N., W.J. Grenney, and D. Whitley, 1994. Neural network for river flow prediction. Journal of Computing in Civil Engineering 8: 201-220. Kitzmiller, D.H., 1996. One-hour forecasts of radar-estimated rainfall by an extrapolative-statistical method. TDL Office Note 96-1, National Weather Service, NOAA, US Department of Commerce, 26 pp. Kohonen, T., 1982. Self-organized formation of topologically correct feature maps. Biological Cybernetics 43: 59-69. Koizumi, K., 1999. An objective method to modify numerical model forecasts with newly given weather data using an artificial neural network. Weather and Forecasting 14: 109-118. Kuligowski R.J. and A. Barros, 1998. Localized precipitation forecasts from a numerical weather prediction model using artificial neural networks. Weather and forecasting 13: 1194-1204. Kuligowski R.J. and A. Barros, 2001. Combined IR-microwave satellite retrieval of temperature and dewpoint profiles using artificial neural networks. Journal of Applied Meteorology 40: 2051-2067. Lallahem, S. and J. Mania, 2003. Evaluation and forecasting of daily groundwater outflow in a small chalky watershed, Hydrological Processes 17: 1561-1577. Luk, K.C., J.E. Ball, and A. Sharma, 2000. A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting. Journal of Hydrology 227: 56-65. Maier, H.R. and G.C. Dandy, 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modeling and Software 15(1): 101-124. Marshall, J.M. and W.M.K. Palmer, 1948. The distribution of raindrops with size. Journal of Applied Meteorology 5: 165-166. McCuen, R.H., 1985. Statistical methods for engineers. Prentice-Hall, Englewood Cliffs, N.J. McCulloch, W.S. and W. Pitts, 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5: 115-133. Mecklenburg, S., J. Joss, and W. Schmid, 2000. Improving the nowcasting of precipitation in an Alpine region with an enhancedradar echo tracking algorithm. Journal of Hydrology 239: 46-68. Miller, L.J., C.G. Mohr, and A. Weinheimer, 1986. The simple rectification in Cartesian space of folded radial velocities from Doppler radar sampling. Journal of Atmospheric and Oceanic Technology 1: 162-174. Mimikou, M.A. and E.A. Baltas, 1996. Flood forecasting based on radar rainfall measurements. Journal of Water Resources Planning and Management 122: 151-156. Minns, A.W. and M.J. Hall, 1996. Artificial neural networks as rainfall-runoff models. Hydrological Sciences Journal 41(3): 399-417. Morin, E., W.F. Krajewski, D.C. Goodrich, X. Gao, and S. Sorooshian, 2003. Estimating rainfall intensities from weather radar data: the scale-dependency problem. Journal of Hydrometeorology 4: 782-797. Nash, J.E. and J.V. Sutcliffe, 1970. River flow forecasting through conceptual maodels, part I - a discussion of principles. Journal of Hydrology 10: 282-290. Parlos, A.G., O.T. Rais, and A.F. Atiya, 2000. Multi-step-ahead prediction using dynamic recurrent neural networks. Neural Networks 13: 765-786. Pierce, C.E., C.G. Collier, P.J. Hardaker, and C.M. Haggett, 2000. GANDOLF: A system for generating automated nowcasts of convective precipitation. Meteorological Application 8: 341-360. Powell, M.J.D., 1985. Radial basis functions for multivariable interpolation: A review. IMA Conference on Algorithms for the Approximation of Functions and Data, 143-167, RMCS, Shrivenham, England. Ranjithan, S., J.W. Eheart, and J.H. Garrett Jr, 1993. Neural network-based screening for groundwater reclamation under uncertainty. Water Resources Research 29(3): 563-574. Rogers, R.R. and M.K. Yau, 1989. A short course in cloud physics. Pergamon, 293 pp. Rumelhart, D.E., G.E. Hinton, and R.J. Williams, 1986. Learning internal representations by error propagation. Parallel Distributed Processing 1: 318-362. Sajikumar, N. and B.S. Thandaveswara, 1999. A non-linear rainfall-runoff model using an artificial neural network. Journal of Hydrology 216: 32-55. Salas, J.D., J.W. Delleur, V. Yevjevich and W.L. Lane, 1985. Applied modeling of hydrologic time series. Water Resources Publications. Sauvageot, H., 1992. Radar Meteorology. Artech House, 366 pp. Schenker, B. and M. Agarwal, 1995. Long-range prediction for poorly-known systems. International Journal of Control 62: 227-238. Seibert, J., 2001. On the need for benchmarks in hydrological modeling. Hydrological Processes 15: 1063-1064. Selvaraj, R., P.B. Deshpande, S.S. Tambe, and B.D. Kulkarni, 1995. Neural networks for the identification of MSF desalination plants. Desalination 101: 185-193. Shamseldin, A.Y., 1997. Application of a neural network technique to rainfall-runoff modeling. Journal of Hydrology 199: 272-294. Sivakumar, B., 2005. Hydrologic modeling and forecasting: role of thresholds. Environmental Modeling & Software 20(5): 515-519. Sokol Z., 2006. Nowcasting of 1-h precipitation using radar and NWP data. Journal of Hydrology 328: 200-211. Sorooshian, S., K.L. Hsu, X. Gao, H.V. Gupta, B. Imam, and D. Braithwaite, 2000. Evaluation of PERSIANN system satellite-based estimates of tropical rain. Bulletin of the American Meteorological Society 81: 2035-2046. Su, H.T., T.J. McAvoy, and P. Werbos, 1992. Long-term prediction of chemical processes using recurrent neural networks: A parallel training approach. Industrial Applications of Chemical Engineering Research 31: 1338-1352. Tokar, A.S. and P.A. Johnson, 1999. Rainfall-runoff modeling using artificial neural networks. Journal of Hydrologic Engineering 4(3): 232-239. Toth, E., A. Brath, and A. Montanari, 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology 239: 132-147. Trafalis, T.B., M.B. Richman, A. White, and B. Santosa, 2002. Data mining techniques for improved WSR-88D rainfall estimation. Computers & Industrial Engineering 43: 775-786. Xiao, R. and V. Chandrasekar, 1997. Development of a neural network based algorithm for rainfall estimation from radar observations. IEEE Transactions on Geoscience and Remote Sensing 35: 160-171. Zealand, C.M., D.H. Burn, and S.P. Simonovic, 1999. Short term stream flow forecasting using artificial neural networks. Journal of Hydrology 214: 32-48.; en-US; http://ntur.lib.ntu.edu.tw/handle/246246/56138; http://ntur.lib.ntu.edu.tw/bitstream/246246/56138/1/ntu-96-D91622006-1.pdf

الاتاحة: http://ntur.lib.ntu.edu.tw/handle/246246/56138
http://ntur.lib.ntu.edu.tw/bitstream/246246/56138/1/ntu-96-D91622006-1.pdf

المؤلفون: 陳美心, Chen, Mei-Hsin

المساهمون: 周仲島, 臺灣大學：大氣科學研究所

مصطلحات موضوعية: 熱帶降雨潛勢, 定量降雨預報, 校驗, 緊鄰雨區誤差分解, Tropical Rainfall Potential, Quantitative Precipitation Forecast, QPESUMS, Validation, Contiguous Rain Area error decomposition

وصف الملف: 2822430 bytes; application/pdf

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http://ntur.lib.ntu.edu.tw/bitstream/246246/50029/1/ntu-95-R93229004-1.pdf