Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific
Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is sti...
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doaj-ea8d8c15acbe46238fbc7ce250fa5f012020-11-24T23:53:28ZengMDPI AGAtmosphere2073-44332019-06-0110634110.3390/atmos10060341atmos10060341Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North PacificQingwen Jin0Xiangtao Fan1Jian Liu2Zhuxin Xue3Hongdeng Jian4Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaBeijing Jinghang Computation and Communication Research Institute, Beijing 100074, ChinaKey Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaCoastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction.https://www.mdpi.com/2073-4433/10/6/341eXtreme Gradient BOOSTing (XGBOOST)Western North Pacifictropical cycloneintensity |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qingwen Jin Xiangtao Fan Jian Liu Zhuxin Xue Hongdeng Jian |
spellingShingle |
Qingwen Jin Xiangtao Fan Jian Liu Zhuxin Xue Hongdeng Jian Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific Atmosphere eXtreme Gradient BOOSTing (XGBOOST) Western North Pacific tropical cyclone intensity |
author_facet |
Qingwen Jin Xiangtao Fan Jian Liu Zhuxin Xue Hongdeng Jian |
author_sort |
Qingwen Jin |
title |
Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific |
title_short |
Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific |
title_full |
Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific |
title_fullStr |
Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific |
title_full_unstemmed |
Using eXtreme Gradient BOOSTing to Predict Changes in Tropical Cyclone Intensity over the Western North Pacific |
title_sort |
using extreme gradient boosting to predict changes in tropical cyclone intensity over the western north pacific |
publisher |
MDPI AG |
series |
Atmosphere |
issn |
2073-4433 |
publishDate |
2019-06-01 |
description |
Coastal cities in China are frequently hit by tropical cyclones (TCs), which result in tremendous loss of life and property. Even though the capability of numerical weather prediction models to forecast and track TCs has considerably improved in recent years, forecasting the intensity of a TC is still very difficult; thus, it is necessary to improve the accuracy of TC intensity prediction. To this end, we established a series of predictors using the Best Track TC dataset to predict the intensity of TCs in the Western North Pacific with an eXtreme Gradient BOOSTing (XGBOOST) model. The climatology and persistence factors, environmental factors, brainstorm features, intensity categories, and TC months are considered inputs for the models while the output is the TC intensity. The performance of the XGBOOST model was tested for very strong TCs such as Hato (2017), Rammasum (2014), Mujiage (2015), and Hagupit (2014). The results obtained show that the combination of inputs chosen were the optimal predictors for TC intensification with lead times of 6, 12, 18, and 24 h. Furthermore, the mean absolute error (MAE) of the XGBOOST model was much smaller than the MAEs of a back propagation neural network (BPNN) used to predict TC intensity. The MAEs of the forecasts with 6, 12, 18, and 24 h lead times for the test samples used were 1.61, 2.44, 3.10, and 3.70 m/s, respectively, for the XGBOOST model. The results indicate that the XGBOOST model developed in this study can be used to improve TC intensity forecast accuracy and can be considered a better alternative to conventional operational forecast models for TC intensity prediction. |
topic |
eXtreme Gradient BOOSTing (XGBOOST) Western North Pacific tropical cyclone intensity |
url |
https://www.mdpi.com/2073-4433/10/6/341 |
work_keys_str_mv |
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