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.

中文翻译：

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CNN-LSTM混合模型用于台风形成预报

台风是一种极端天气事件，会在沿海地区及其他地区造成巨大的生命损失和经济损失。结果，寻求更准确的台风形成预测模型；并且，随着气象学家，政府和其他机构寻求减轻这些灾难性事件的影响，强度已成为当务之急。本文认为，尽管在该领域的工作已经取得了积极的进展，但现有模型仍然存在缺陷。基于流体力学的传统数值预报模型很难预测台风强度。基于统计和机器学习的预测未能考虑到台风形成变量之间的时空关系，从而导致该模型的预测能力较弱。因此，我们提出了一种混合模型，我们认为，可以专注于大气和海洋变量的时空相关性，因此可以对台风“行为”做出更真实​​，更准确的解释。我们的CNN-LSTM模型引入3D卷积神经网络（3DCNN）和2D卷积神经网络（2DCNN）作为更好地了解台风形成特征的空间关系的一种方法。我们还使用LSTM检验台风进程关系的时间序列。基于三个数据集的广泛实验表明，我们的CNN-LSTM混合模型优于现有方法，包括许多官方组织使用的数值预测模型；以及基于统计预测和机器学习的方法。