Next Article in Journal
Accounting for Attribute Non-Attendance and Common-Metric Aggregation in the Choice of Seat Belt Use, a Latent Class Model with Preference Heterogeneity
Next Article in Special Issue
Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
Previous Article in Journal
Identifying and Ranking Influential Nodes in Complex Networks Based on Dynamic Node Strength
Previous Article in Special Issue
The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
Article

Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method

by 1,†, 1,†, 1,*,†, 2 and 2
1
School of Software Engineering, Tongji University, Shanghai 201804, China
2
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Lukasz Machura
Algorithms 2021, 14(3), 83; https://doi.org/10.3390/a14030083
Received: 14 January 2021 / Revised: 23 February 2021 / Accepted: 26 February 2021 / Published: 4 March 2021
(This article belongs to the Special Issue Algorithms and Applications of Time Series Analysis)
A typhoon is an extreme weather event with strong destructive force, which can bring huge losses of life and economic damage to people. Thus, it is meaningful to reduce the prediction errors of typhoon intensity forecasting. Artificial and deep neural networks have recently become widely used for typhoon forecasting in order to ensure typhoon intensity forecasting is accurate and timely. Typhoon intensity forecasting models based on long short-term memory (LSTM) are proposed herein, which forecast typhoon intensity as a time series problem based on historical typhoon data. First, the typhoon intensity forecasting models are trained and tested with processed typhoon data from 2000 to 2014 to find the optimal prediction factors. Then, the models are validated using the optimal prediction factors compared to a feed-forward neural network (FNN). As per the results of the model applied for typhoons Chan-hom and Soudelor in 2015, the model based on LSTM using the optimal prediction factors shows the best performance and lowest prediction errors. Thus, the model based on LSTM is practical and meaningful for predicting typhoon intensity within 120 h. View Full-Text
Keywords: typhoon intensity; neural network; LSTM; time series; rolling forecast typhoon intensity; neural network; LSTM; time series; rolling forecast
Show Figures

Figure 1

MDPI and ACS Style

Yuan, S.; Wang, C.; Mu, B.; Zhou, F.; Duan, W. Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method. Algorithms 2021, 14, 83. https://doi.org/10.3390/a14030083

AMA Style

Yuan S, Wang C, Mu B, Zhou F, Duan W. Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method. Algorithms. 2021; 14(3):83. https://doi.org/10.3390/a14030083

Chicago/Turabian Style

Yuan, Shijin, Cheng Wang, Bin Mu, Feifan Zhou, and Wansuo Duan. 2021. "Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method" Algorithms 14, no. 3: 83. https://doi.org/10.3390/a14030083

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop