A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods. (October 2016)
- Record Type:
- Journal Article
- Title:
- A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods. (October 2016)
- Main Title:
- A prediction scheme for the frequency of summer tropical cyclone landfalling over China based on data mining methods
- Authors:
- Geng, Huantong
Shi, Dawei
Zhang, Wei
Huang, Chao - Abstract:
- ABSTRACT: This study examines the landfalling tropical cyclones (TCs) over C hina using state‐of‐the‐art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (CART)). Using the 1951–2012 TC best track dataset released by the Shanghai Typhoon Institute of the Chinese Meteorological Administration, the tracks of TCs landfalling over the Chinese coast were classified into three clusters through an FMM. Several climate indices were analysed using the CART algorithm for the three clusters. The prediction model built by CART for summer track frequency was based on a random sampling of the data for 46 years (about 75% of the total years) as the training set with a training accuracy of 100% (Cluster‐1), 89.96% (Cluster‐2) and 100% (Cluster‐3). Data for the remaining 16 years (about 25%) were used for testing with a prediction accuracy of 87.5% (Cluster‐1), 62.5% (Cluster‐2) and 68.75% (Cluster‐3). This study focuses on Cluster‐1 of summer TCs landfalling over China for its high frequency, strong intensity, severe impacts and long lifespan. Furthermore, it suggests that the FMM algorithm is effective for track classification of TCs landing over China. In addition, the CART algorithm, which was used to build the prediction model of Cluster‐1 for the classification of track frequency, showed high accuracy and its results can be explained and understood easily. It provides a novel framework for forecasting the frequencyABSTRACT: This study examines the landfalling tropical cyclones (TCs) over C hina using state‐of‐the‐art data mining methods (i.e. Finite Mixture Model (FMM) based cluster algorithm and the Classification and Regression Tree (CART)). Using the 1951–2012 TC best track dataset released by the Shanghai Typhoon Institute of the Chinese Meteorological Administration, the tracks of TCs landfalling over the Chinese coast were classified into three clusters through an FMM. Several climate indices were analysed using the CART algorithm for the three clusters. The prediction model built by CART for summer track frequency was based on a random sampling of the data for 46 years (about 75% of the total years) as the training set with a training accuracy of 100% (Cluster‐1), 89.96% (Cluster‐2) and 100% (Cluster‐3). Data for the remaining 16 years (about 25%) were used for testing with a prediction accuracy of 87.5% (Cluster‐1), 62.5% (Cluster‐2) and 68.75% (Cluster‐3). This study focuses on Cluster‐1 of summer TCs landfalling over China for its high frequency, strong intensity, severe impacts and long lifespan. Furthermore, it suggests that the FMM algorithm is effective for track classification of TCs landing over China. In addition, the CART algorithm, which was used to build the prediction model of Cluster‐1 for the classification of track frequency, showed high accuracy and its results can be explained and understood easily. It provides a novel framework for forecasting the frequency of TCs landfalling over China. … (more)
- Is Part Of:
- Meteorological applications. Volume 23:Number 4(2016)
- Journal:
- Meteorological applications
- Issue:
- Volume 23:Number 4(2016)
- Issue Display:
- Volume 23, Issue 4 (2016)
- Year:
- 2016
- Volume:
- 23
- Issue:
- 4
- Issue Sort Value:
- 2016-0023-0004-0000
- Page Start:
- 587
- Page End:
- 593
- Publication Date:
- 2016-10
- Subjects:
- Cluster‐1 TC track -- FMM algorithm -- CART algorithm
Meteorology -- Periodicals
Meteorological services -- Periodicals
551.5 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1469-8080 ↗
http://onlinelibrary.wiley.com/ ↗ - DOI:
- 10.1002/met.1580 ↗
- Languages:
- English
- ISSNs:
- 1350-4827
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5705.280000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 1259.xml