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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Bibliographic Details
Published in:Natural hazards (Dordrecht) Vol. 91; no. 1; pp. 201 - 220
Main Authors: Huang, Ying, Jin, Long, Zhao, Hua-sheng, Huang, Xiao-yan
Format: Journal Article
Language:English
Published: Dordrecht Springer Netherlands 01.03.2018
Springer Nature B.V
Subjects:
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
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Summary: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, spanning 1980–2015 are used for model development. The FNN model input is constructed from potential predictors by employing both a stepwise regression method (SRM) and a locally linear embedding (LLE) algorithm. The LLE algorithm is capable of finding meaningful low-dimensional architectures hidden in their nonlinear high-dimensional data space and separating the underlying factors. In this scheme, the newly developed model, which is termed the FNN–LLE model, is used for daily TC precipitation prediction from 20:00 (Beijing Time, or BT) of the previous day to 20:00 BT of the current day at 89 stations covering Guangxi, China. Using identical modeling samples and independent samples, predictions of the FNN–LLE model are compared with the widely used SRM and interpolation method using the fine-mesh data of the European Centre for Medium-Range Weather Forecasts (ECMWF) in terms of the performance of TC rainfall prediction at 89 stations in Guangxi. The root-mean-square error (RMSE), bias, and equitable threat score (ETS) results were employed to assess the predicted outcomes. Results show that the FNN–LLE model is superior to the interpolation method by ECMWF and SRM for TC precipitation prediction with RMSE values of 21.94, 24.07, and 25.22 in FNN–LLE model, interpolation method by ECMWF and SRM, respectively. Moreover, FNN–LLE model having average bias and ETS values close to 1.0 gave better predictions than did the interpolation method by ECMWF and SRM.
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ISSN:0921-030X
1573-0840
DOI:10.1007/s11069-017-3122-x