A Study on Typhoon Risk Prediction by Different Methods of Pattern Recognition

Chen, Wang-Kun; Sui, GuangJun; Tang, Danling

doi:10.1007/978-3-642-40695-9_23

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…For the statistical models, many scholars paid attention to establishing mathematical relationships such as regression or exponent functions to pre-assess disaster risks and damage [15][16][17][18][19] . Some studies have integrated recent advances in fuzzy mathematics, grey theory and information spread technique in order to assess typhoon disaster risks 20,21 . With the rapid development of arti cial intelligence, some neural network and support vector machine models have been developed [22][23][24] .…”

mentioning

confidence: 99%

Development of a Dynamical Statistical Analog Ensemble Forecast Model for Landfalling Typhoon Disasters

Ren

Zhang

et al. 2023

Preprint

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In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: i) obtaining the forecast track of a target landfalling typhoon, ii) constructing its generalized initial value (GIV), iii) identifying its analogs based on the GIV, and iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.

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confidence: 99%

Development of a Dynamical Statistical Analog Ensemble Forecast Model for Landfalling Typhoon Disasters

Ren

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters

Wu,

Ren,

Zhang

et al. 2023

Sci Rep

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In this report, the development of a Dynamical Statistical Analog Ensemble Forecast model for landfalling typhoon disasters (LTDs) and some applications over coastal China are described. This model consists of the following four elements: (i) obtaining the forecast track of a target landfalling typhoon, (ii) constructing its generalized initial value (GIV), (iii) identifying its analogs based on the GIV, and (iv) assembling typhoon disasters of the analogs. Typhoon track, intensity, and landfall date are introduced in GIV at this early development stage. The pre-assessment results show that the mean threat scores of two important damage levels of LTDs reach 0.48 and 0.55, respectively. Of significance is that most of the damage occurs near the typhoon centers around the time of landfall. These results indicate the promising performance of the model in capturing the main damage characteristics of typhoon disasters, which would help coastal community mitigate damage from destructive typhoons.

show abstract

A Study on Typhoon Risk Prediction by Different Methods of Pattern Recognition

Cited by 2 publications

References 11 publications

Development of a Dynamical Statistical Analog Ensemble Forecast Model for Landfalling Typhoon Disasters

Development of a Dynamical Statistical Analog Ensemble Forecast Model for Landfalling Typhoon Disasters

Development of a dynamical statistical analog ensemble forecast model for landfalling typhoon disasters

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