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A hybrid CNN-LSTM model for typhoon formation forecasting

Chen, Rui ; Wang, Xiang ; Zhang, Weimin ; Zhu, Xiaoyu ; Li, Aiping ; Yang, Chao

GeoInformatica, 2019-07, Vol.23 (3), p.375-396 [Periódico revisado por pares]

New York: Springer US

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Título:
A hybrid CNN-LSTM model for typhoon formation forecasting
Autor: Chen, Rui ; Wang, Xiang ; Zhang, Weimin ; Zhu, Xiaoyu ; Li, Aiping ; Yang, Chao
Assuntos: Analysis ; Artificial intelligence ; Artificial neural networks ; Atmospheric models ; Catastrophic events ; Coastal zone ; Computational fluid dynamics ; Computational linguistics ; Computer Science ; Data Structures and Information Theory ; Earth and Environmental Science ; Economics ; Extreme weather ; Fluid mechanics ; Geographical Information Systems/Cartography ; Geography ; Hurricanes ; Information Storage and Retrieval ; Language processing ; Learning algorithms ; Learning behaviour ; Machine learning ; Mathematical models ; Meteorologists ; Multimedia Information Systems ; Natural language interfaces ; Neural networks ; Numerical methods ; Numerical prediction ; Organizations ; Prediction models ; Statistical analysis ; Statistical methods ; Three dimensional models ; Two dimensional models ; Typhoons ; Weather ; Weather forecasting
É parte de: GeoInformatica, 2019-07, Vol.23 (3), p.375-396
Descrição: A typhoon is an extreme weather event that can cause huge loss of life and economic damage in coastal areas and beyond. As a consequence, the search for more accurate predictive models of typhoon formation; and, intensity have become imperative as meteorologists, governments, and other agencies seek to mitigate the impact of these catastrophic events. While work in this field has progressed diligently, this paper argues, that the existing models are deficient. Traditional numerical forecast models based on fluid mechanics have difficulty in predicting the intensity of typhoons. Forecasts based on statistics and machine learning fail to take into account the spatial and temporal relationships among typhoon formation variables leading to weaknesses in the predictive power of this model. Therefore, we propose a hybrid model, which we argue, can produce a more realist and accurate account of typhoon ‘behavior’ as it focuses on both the spatio-temporal correlations of atmospheric and oceanographic variables. Our CNN-LSTM model introduces 3D convolutional neural networks (3DCNN) and 2D convolutional neural networks (2DCNN) as a method to better understand the spatial relationships of the features of typhoon formation. We also use LSTM to examine the temporal sequence of relations in typhoon progression. Extensive experiments based on three datasets show that our hybrid CNN-LSTM model is superior to existing methods, including numerical forecast models used by many official organizations; and, statistical forecast and machine learning based methods.
Editor: New York: Springer US
Idioma: Inglês

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A hybrid CNN-LSTM model for typhoon formation forecasting

Chen, Rui ; Wang, Xiang ; Zhang, Weimin ; Zhu, Xiaoyu ; Li, Aiping ; Yang, Chao

New York: Springer US

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