The practical research on flood forecasting based on artificial neural networks
作者： Li-Hua Feng , Jia Lu
DOI: 10.1016/J.ESWA.2009.09.037
关键词: Data mining 、 Novelty 、 Machine learning 、 Artificial neural network 、 Nonlinear system 、 Computer science 、 Flood forecasting 、 Artificial intelligence 、 Sensitivity (control systems) 、 Flood myth
摘要: The technologies of artificial neural networks can be used to complete information processing the through interaction cells. mappings stimuli effects and input output estimates are obtained via combinations nonlinear functions. This offers advantages self-learning, self-organization, self-adaptation fault tolerance. It also has possibility use in applications for flood forecasting. Furthermore, ANN technology allows us multiple variables both layers. is very important calculation since stage, discharge, other hydrological often functions many influential variables, which form novelty value paper. For this research, authors proposed a new forecasting system with related applications, based on method. method been shown offer better results performance efficiency. expected that application will increase sensitivity further performance.
elsevier.com
sci-hub.se 参考文章(12)
Chat Srivaree-ratana, Abdullah Konak, Alice E. Smith, Estimation of all-terminal network reliability using an artificial neural network Computers & Operations Research. ,vol. 29, pp. 849- 868 ,(2002) , 10.1016/S0305-0548(00)00088-5
Mustafa E. Üreyen, Pelin Gürkan, Comparison of artificial neural network and linear regression models for prediction of ring spun yarn properties. I. Prediction of yarn tensile properties Fibers and Polymers. ,vol. 9, pp. 87- 91 ,(2008) , 10.1007/S12221-008-0014-4
MARINA CAMPOLO, ALFREDO SOLDATI, PAOLO ANDREUSSI, Artificial neural network approach to flood forecasting in the river Arno Hydrological Sciences Journal-journal Des Sciences Hydrologiques. ,vol. 48, pp. 381- 398 ,(2003) , 10.1623/HYSJ.48.3.381.45286
R. L. WILBY, R. J. ABRAHART, C. W. DAWSON, Detection of conceptual model rainfall—runoff processes inside an artificial neural network Hydrological Sciences Journal-journal Des Sciences Hydrologiques. ,vol. 48, pp. 163- 181 ,(2003) , 10.1623/HYSJ.48.2.163.44699
Amitabha Mukhopadhyay, Application of visual, statistical and artificial neural network methods in the differentiation of water from the exploited aquifers in Kuwait Hydrogeology Journal. ,vol. 11, pp. 343- 356 ,(2003) , 10.1007/S10040-003-0257-5
V. N. Mikhailov, V. N. Morozov, N. I. Cheroy, M. V. Mikhailova, Ye. F. Zav'yalova, Extreme flood on the Danube River in 2006 Russian Meteorology and Hydrology. ,vol. 33, pp. 48- 54 ,(2008) , 10.3103/S1068373908010081
R. Lippmann, An introduction to computing with neural nets IEEE ASSP Magazine. ,vol. 4, pp. 4- 22 ,(1987) , 10.1109/MASSP.1987.1165576
Konstantin Loukachine, Norman G. Loeb, Application of an Artificial Neural Network Simulation for Top-of-Atmosphere Radiative Flux Estimation from CERES Journal of Atmospheric and Oceanic Technology. ,vol. 20, pp. 1749- 1757 ,(2003) , 10.1175/1520-0426(2003)020<1749:AOAANN>2.0.CO;2
Wendy Kenyon, Gary Hill, Peter Shannon, Scoping the role of agriculture in sustainable flood management Land Use Policy. ,vol. 25, pp. 351- 360 ,(2008) , 10.1016/J.LANDUSEPOL.2007.09.003
U. Rajendra Acharya, P. Subbanna Bhat, S.S. Iyengar, Ashok Rao, Sumeet Dua, Classification of heart rate data using artificial neural network and fuzzy equivalence relation Pattern Recognition. ,vol. 36, pp. 61- 68 ,(2003) , 10.1016/S0031-3203(02)00063-8