# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 1 Introduction
For wind speed forecasting, some researchers have tried to reduce prediction errors by using a hybrid of the population particle algorithm and a multi-quantile robust extreme learning machine ([PERSON] et al., 2019), wavelet decomposition and wavelet neural networks optimized by the Cuckoo search algorithm ([PERSON], 2018), and an ANN combined with the discrete wavelet transform method ([PERSON], 2018). In terms of the results, all of the hybrid methods improved the prediction results of wind speed forecasting with few prediction steps (no more than five steps) and a short lead time generally. Moreover, the prediction factor of these studies was only wind speed. Thus, for typhoon intensity forecasting, it is still necessary to find a practical method with more prediction steps and a long lead time. With the development of a neural network, especially a deep neural network (DNN), some researchers have tried to study the problem of typhoons based on a DNN, such as a convolution neural network (CNN) ([PERSON] and [PERSON], 2017; [PERSON] and [PERSON], 2018) and long short-term memory (LSTM) ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018), but not a simple ANN. [PERSON] and [PERSON] (2018) applied LSTM to forecast wind speed for the next 10 min and achieved a high prediction accuracy rate of more than 98%. [PERSON] et al. (2018) designed a wind speed prediction model based on the fuzzy information granulation method and LSTM optimized by the Adam algorithm, for which the prediction results of wind speed for the next 1 h were better than the models based on a support vector machine (SVM) and an ANN using the back-propagation algorithm (BPNN). [PERSON] and [PERSON] (2018) proposed a wind speed prediction model based on LSTM using the wavelet transform, feature selection, and crow search algorithms, which showed the best performance for the wind speed in the future 1 h compared to other models based on LSTM not using these algorithms. [PERSON] et al. (2018) proposed a wind speed prediction model based on LSTM using the attention mechanism, wavelet decomposition, and variational mode decomposition methods. Compared to models based on the autoregressive moving average and support vector regression, the proposed prediction model was stable and had cumulative errors within the next 5 h every 1 h. These studies aimed to predict short-term wind speed and to show that prediction models based on LSTM are better for wind speed prediction problems, but they all only used wind speed data for the experiments and were not in the field of typhoon intensity forecasting. Because of the complexity of typhoon forecasting and the recent improvements of LSTM, only a few papers have used prediction models based on LSTM to predict typhoon intensity. Additionally, the study of typhoon intensity forecasting based on LSTM is only applicable to the next 24 h [21], which shows that there is an important problem with forecasting typhoon intensity for a longer period of time. Based on the above problems regarding typhoon intensity forecasting and prediction models based on neural networks, typhoon intensity forecasting models based on LSTM are proposed. In addition, considering the development processes of typhoon cases, in this experiment, typhoon intensity is forecast as a time series problem. During the training and test phases, optimal prediction factors are selected for typhoon intensity forecasting. After determining the optimal prediction factors for typhoon intensity forecasting, prediction models based on LSTM using the optimal prediction factors are applied for typhoons Chan-hom and Soudelor in 2015. Finally, these models are validated against a model based on an FNN. The remainder of this paper is organized as follows: Section 2 introduces the study area and processed data of the experiment. Section 3 describes the methods to develop the typhoon intensity forecasting models based on LSTM and FNN and the evaluation indicators to validate these models. The forecasting models based on LSTM are presented in Section 4.