Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones: comparisons with interpolation method by ECMWF and stepwise regression method

A tropical cyclone (TC) precipitation prediction scheme has been developed based on the physical quantities of the NCEP/NCAR reanalysis data as potential predictors and using fuzzy neural network (FNN) model. TC precipitation samples from 172 tropical cyclones (TCs) affecting Guangxi, China, spannin...

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Veröffentlicht in:	Natural hazards (Dordrecht) Jg. 91; H. 1; S. 201 - 220
Hauptverfasser:	Huang, Ying, Jin, Long, Zhao, Hua-sheng, Huang, Xiao-yan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.03.2018 Springer Nature B.V
Schlagworte:	Algorithms Artificial neural networks Atmospheric precipitations Bias Civil Engineering Cyclones Data Earth and Environmental Science Earth Sciences Embedding Environmental Management Finite element method Fuzzy logic Geophysics/Geodesy Geotechnical Engineering & Applied Earth Sciences Hurricanes Hydrogeology Interpolation Mathematical models Modelling Natural Hazards NCEP/NCAR reanalysis Neural networks Original Paper Precipitation Predictions Rain Rainfall Rainfall forecasting Root-mean-square errors Stations Tropical climate Tropical cyclones Weather forecasting China Interpretation and application of ECMWF Fuzzy neural network Locally linear embedding algorithm Forecasting techniques Tropical cyclone precipitation prediction Quantitative precipitation forecasts
ISSN:	0921-030X, 1573-0840
Online-Zugang:	Volltext
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