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

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Abstract

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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Abbreviations

BIAS:

Bias score

BPNN:

Back-propagation neural network

ECMWF:

European Centre for Medium-Range Weather Forecasts

ETS:

Equitable threat score

FNN:

Fuzzy neural network

FNN–LLE:

FNN with employing both stepwise regression method and LLE algorithm for constructing model input

Guangxi:

Guangxi Zhuang Autonomous Region, China

LLE:

Locally linear embedding

MDS:

Multidimensional scaling

NWP:

Numerical weather prediction

PCA:

Principal component analysis

RMSE:

Root-mean-square error

SRM:

Stepwise regression method

TC:

Tropical cyclone

TCs:

Tropical cyclones

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant 41575051, Grant 41765002 and Grant 41565005), and the Program of Guangxi Meteorological Service (Grant 2017M08 and Grant 2016Z03).

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Authors and Affiliations

  1. Guangxi Research Institute of Meteorological Disasters Mitigation, 81 Minzu Road, Nanning, China

    Ying Huang, Long Jin, Hua-sheng Zhao & Xiao-yan Huang

Authors
  1. Ying Huang
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  2. Long Jin
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  3. Hua-sheng Zhao
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  4. Xiao-yan Huang
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Correspondence to Ying Huang.

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Huang, Y., Jin, L., Zhao, Hs. et al. Fuzzy neural network and LLE Algorithm for forecasting precipitation in tropical cyclones: comparisons with interpolation method by ECMWF and stepwise regression method. Nat Hazards 91, 201–220 (2018). https://doi.org/10.1007/s11069-017-3122-x

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  • DOI: https://doi.org/10.1007/s11069-017-3122-x

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