Typhoon Track Prediction by a Support Vector Machine Using Data Reduction Methods

Song, Hee-Jun; Huh, Sung-Hoe; Kim, Joo-Hong; Ho, Chang-Hoi; Park, Sojung

doi:10.1007/11596448_74

Cited by 16 publications

(10 citation statements)

References 5 publications

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“…In this paper, we suggest a prediction method based on statistical models. Contrary to previous works except Song et al (2005), our model has a simpler concept by using the data intuitively. We use 2-dimensional wind field data obtained by averaging vertical values of 3-dimensional wind field data from NCEP.…”

Section: Previous Workmentioning

confidence: 97%

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Bayesian Typhoon Track Prediction Using Wind Vector Data

Han

Lee

2015

CSAM

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In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

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Section: Previous Workmentioning

confidence: 97%

“…There are rarely prediction methods using statistical model only. Song et al (2005) shows that it is possible to deal with a typhoon track using data obtained eidetically. They suggest a prediction model using raw wind field data.…”

Section: Previous Workmentioning

confidence: 99%

Bayesian Typhoon Track Prediction Using Wind Vector Data

Han

Lee

2015

CSAM

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“…In order to select the best of these eight situations in Table 4, an evaluation method is introduced, with which the Index of each situation is calculated, where Index is defined according to formula (6). For any situation, denotes the number of areas of divided research object and AJ , FA , and FD denote the AJ , FA , and FD of the th area, respectively.…”

Section: The Formation Of Landing Criterions In Five Areasmentioning

confidence: 99%

“…The main methods for traditional TC forecast contain statistical methods and dynamic methods, most of which are along with complicated processes or lower precision. The statistical methods use the historical TCs' positions, intensity, and so on to predict TC's characteristic factors, such as fuzzy multicriteria decision support model [2], conditional nonlinear optimal perturbation, first singular vector, ensemble transform Kalman filter [3], back propagation-neural network [4], adaptive neural network classifier using a two-layer feature selector [5], and a support vector machine using data reduction methods [6]. Dynamic methods are mainly based on numerical forecast, such as a simplified dynamical system based on a logistic growth equation (LGE) [7], a regional coupled atmosphere-ocean model [8], the PSU-NCAR Mesoscale Model version 5 [9], and the GFDL 25-km-Resolution Global Atmospheric Model [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Tropical Cyclones’ Characteristic Factors on Hainan Island Using Data Mining Technology

Zhou

Gao

Zhang

et al. 2014

Advances in Meteorology

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A new methodology combining data mining technology with statistical methods is proposed for the prediction of tropical cyclones' characteristic factors which contain latitude, longitude, the lowest center pressure, and wind speed. In the proposed method, the best track datasets in the years 1949∼2012 are used for prediction. Using the method, effective criterions are formed to judge whether tropical cyclones land on Hainan Island or not. The highest probability of accurate judgment can reach above 79%. With regard to TCs which are judged to land on Hainan Island, related prediction equations are established to effectively predict their characteristic factors. Results show that the average distance error is improved compared with the National Meteorological Centre of China.

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“…The attempt to simulate the nonlinear characteristics of tropical cyclones by using the nonlinear feature extraction ability of machine learning has opened a new direction for the follow-up studies of tropical cyclone forecasting. Song et al [6] utilized the kernel method of the SVM to replace the nonlinear feature extraction method of numerical forecasting. This method improved the forecasting accuracy by reducing the input dimension and data, but it did not understand the inherent temporal correlations from the historical data of tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

A Novel Tropical Cyclone Track Forecast Model Based on Attention Mechanism

Fang

et al. 2022

Atmosphere

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Tropical cyclones are one of the most powerful and destructive weather systems on Earth. Accurately forecasting the landing time, location and moving paths of tropical cyclones are of great significance to mitigate the huge disasters it produces. However, with the continuous accumulation of meteorological monitoring data and the application of multi-source data, traditional tropical cyclone track forecasting methods face many challenges in forecasting accuracy. Recently, deep learning methods have proven capable of learning spatial and temporal features from massive datasets. In this paper, we propose a new spatiotemporal deep learning model for tropical cyclone track forecasting, which adopts spatial location and multiple meteorological factors to forecast the tracks of tropical cyclones. The model proposes a multi-layer ConvGRU to extract the nonlinear spatial features of tropical cyclones, while Spatial and Channel Attention Mechanism (CBAM) is adopted to overcome the large-scale problem of high response isobaric surface affecting the tropical cyclones. Meanwhile, this model utilizes a Deep and Cross framework to combine the traditional CNN model with the multi-ConvGRU model. Experiments were conducted on the China Meteorological Administration Tropical Cyclone Best Track Dataset (CMA) from 2000 to 2020, and the EAR-Interim dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The experimental results show that the proposed model is superior to the deep learning tropical cyclone forecasting methods.

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Typhoon Track Prediction by a Support Vector Machine Using Data Reduction Methods

Cited by 16 publications

References 5 publications

Bayesian Typhoon Track Prediction Using Wind Vector Data

Bayesian Typhoon Track Prediction Using Wind Vector Data

Prediction of Tropical Cyclones’ Characteristic Factors on Hainan Island Using Data Mining Technology

A Novel Tropical Cyclone Track Forecast Model Based on Attention Mechanism

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