{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T09:46:42Z","timestamp":1771926402138,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Ministry of Education","award":["2016R1A6A1A03012812"],"award-info":[{"award-number":["2016R1A6A1A03012812"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This paper presents a novel approach for typhoon track prediction that potentially impacts a region using ensemble k-Nearest Neighbor (k-NN) in a GIS environment. In this work, the past typhoon tracks are zonally split into left and right classes by the current typhoon track and then grouped as an ensemble member containing three (left-center-right) typhoons. The proximity of the current typhoon to the left and\/or right class is determined by using a supervised classification k-NN algorithm. The track dataset created from the current and similar class typhoons is trained by using the supervised regression k-NN to predict current typhoon tracks. The ensemble averaging is performed for all typhoon track groups to obtain the final track prediction. It is found that the number of ensemble members does not necessarily affect the accuracy; the determination of similarity at the beginning, however, plays an important key role. A series of tests yields that the present method is able to produce a typhoon track prediction with a fast simulation time, high accuracy, and long duration.<\/jats:p>","DOI":"10.3390\/rs14215292","type":"journal-article","created":{"date-parts":[[2022,10,24]],"date-time":"2022-10-24T10:09:23Z","timestamp":1666606163000},"page":"5292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Regional Typhoon Track Prediction Using Ensemble k-Nearest Neighbor Machine Learning in the GIS Environment"],"prefix":"10.3390","volume":"14","author":[{"given":"Mamad","family":"Tamamadin","sequence":"first","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea"},{"name":"Department of Meteorology, Institut Teknologi Bandung, Bandung 40132, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7070-3332","authenticated-orcid":false,"given":"Changkye","family":"Lee","sequence":"additional","affiliation":[{"name":"University Core Research Center for Disaster-free & Safe Ocean City Construction, Dong-A University, Busan 49315, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7743-4881","authenticated-orcid":false,"given":"Seong-Hoon","family":"Kee","sequence":"additional","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea"},{"name":"University Core Research Center for Disaster-free & Safe Ocean City Construction, Dong-A University, Busan 49315, Korea"}]},{"given":"Jurng-Jae","family":"Yee","sequence":"additional","affiliation":[{"name":"Department of ICT Integrated Ocean Smart Cities Engineering, Dong-A University, Busan 49315, Korea"},{"name":"University Core Research Center for Disaster-free & Safe Ocean City Construction, Dong-A University, Busan 49315, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, R., Zhang, W., and Wang, X. (2020). Machine learning in tropical cyclone forecast modeling: A review. Atmosphere, 11.","DOI":"10.3390\/atmos11070676"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106260","DOI":"10.1016\/j.oceaneng.2019.106260","article-title":"Wind forcing effect on hindcasting of typhoon-driven extreme waves","volume":"188","author":"Chen","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_3","unstructured":"Teng, M.C., Su, J.L., and Chien, S.W. (July, January 29). Transportation infrastructure disaster impact and lessons learned after typhoon MORAKOT. Proceedings of the 9th Asia Pacific Transportation Development Conference, Chongqing, China."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"23","DOI":"10.3178\/hrl.6.23","article-title":"A preliminary impact assessment of typhoon wind risk of residential buildings in Japan under future climate change","volume":"6","author":"Nishijima","year":"2012","journal-title":"Hydrol. Res. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jang, D., Joo, W., Jeong, C.H., Kim, W., Park, S.W., and Song, Y. (2020). The Downscaling Study for Typhoon-Induced Coastal Inundation. Water, 12.","DOI":"10.3390\/w12041103"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"684","DOI":"10.2112\/SI65-116.1","article-title":"Projection of extreme typhoon waves: Case study at Busan, Korea","volume":"65","author":"Chun","year":"2013","journal-title":"J. Coast. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11069-009-9396-x","article-title":"Storm surge prediction using an artificial neural network model and cluster analysis","volume":"51","author":"You","year":"2009","journal-title":"Nat. Hazards"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, J.M., Son, K., Yoo, Y., Lee, D., and Kim, D.Y. (2018). Identifying risk indicators of building damage due to typhoons: Focusing on cases of South Korea. Sustainability, 10.","DOI":"10.3390\/su10113947"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.atmosres.2011.09.012","article-title":"Tropical cyclone track forecasting techniques\u2014A review","volume":"104","author":"Roy","year":"2012","journal-title":"Atmos. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF01022522","article-title":"Recent advancements in dynamical tropical cyclone track predictions","volume":"56","author":"Elsberry","year":"1995","journal-title":"Meteorol. Atmos. Phys."},{"key":"ref_11","unstructured":"Jeffries, R.A., and Miller, R.J. (1993). Tropical Cyclone Forecasters Reference Guide, Naval Research Laboratory. Technical Report No. NRL\/PU\/7515-93-0011."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.rser.2013.06.042","article-title":"Review of solar irradiance forecasting methods and a proposition for small-scale insular grids","volume":"27","author":"Diagne","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_13","first-page":"1013","article-title":"Research Progress on China typhoon numerical prediction models and associated major techniques","volume":"29","author":"MA","year":"2014","journal-title":"Prog. Geophys."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chien, T.Y., Chen, S.Y., Huang, C.Y., Shih, C.P., Schwartz, C.S., Liu, Z., Bresch, J., and Lin, J.Y. (2022). Impacts of Radio Occultation Data on Typhoon Forecasts as Explored by the Global MPAS-GSI System. Atmosphere, 13.","DOI":"10.3390\/atmos13091353"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LGRS.2020.3034612","article-title":"Research of the Area of Generation of High-Frequency Infrasound Oscillations in the Sea of Japan, Caused by Typhoons","volume":"19","author":"Dolgikh","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dolgikh, G.I., Chupin, V.A., Gusev, E.S., and Timoshina, G.A. (2021). Cyclonic Process of the \u201cVoice of the Sea\u201d Microseism Generation and Its Remote Monitoring. Remote Sens., 13.","DOI":"10.3390\/rs13173452"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Song, H.J., Huh, S.H., Kim, J.H., Ho, C.H., and Park, S.K. (2005). Typhoon Track Prediction by a Support Vector Machine Using Data Reduction Methods. International Conference on Computational and Information Science, Springer. Computational Intelligence and Security.","DOI":"10.1007\/11596448_74"},{"key":"ref_18","first-page":"45","article-title":"Fast prediction of typhoon tracks based on a similarity method and GIS","volume":"6","author":"Zhen","year":"2013","journal-title":"Disaster Adv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.atmosres.2018.08.018","article-title":"A tropical cyclone similarity search algorithm based on deep learning method","volume":"214","author":"Wang","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1175\/WAF-D-18-0007.1","article-title":"An Objective Track Similarity Index and Its Preliminary Application to Predicting Precipitation of Landfalling Tropical Cyclones","volume":"33","author":"Ren","year":"2018","journal-title":"Weather Forecast."},{"key":"ref_21","unstructured":"National Institute of Informatics (2022, July 15). Digital Typhoon: Typhoon Images and Information. Available online: http:\/\/agora.ex.nii.ac.jp\/digital-typhoon\/index.html.en."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1029\/2018GB005992","article-title":"A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon","volume":"33","author":"Lee","year":"2019","journal-title":"Glob. Biogeochem. Cycles"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100004","DOI":"10.1016\/j.acags.2019.100004","article-title":"A competitive ensemble model for permeability prediction in heterogeneous oil and gas reservoirs","volume":"1","author":"Adeniran","year":"2019","journal-title":"Appl. Comput. Geosci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TIP.2010.2052277","article-title":"K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval","volume":"20","author":"Khelifi","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"103589","DOI":"10.1016\/j.infrared.2020.103589","article-title":"Improving classification performance of four class FNIRS-BCI using Mel Frequency Cepstral Coefficients (MFCC)","volume":"112","author":"Ghaffar","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_26","first-page":"448","article-title":"Efficient implementation of k-nearest neighbor classifier using vote count circuit","volume":"61","author":"Shu","year":"2014","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"108280","DOI":"10.1016\/j.agrformet.2020.108280","article-title":"Improved gap filling approach and uncertainty estimation for eddy covariance N2O fluxes","volume":"297","author":"Goodrich","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Du, K.L., and Swamy, M.N.S. (2014). Clustering I: Basic Clustering Models and Algorithms. Neural Networks and Statistical Learning, Springer.","DOI":"10.1007\/978-1-4471-5571-3_8"},{"key":"ref_29","unstructured":"Graser, A. (2016). Learning Qgis, Packt Publishing Ltd."},{"key":"ref_30","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7412373","DOI":"10.1155\/2016\/7412373","article-title":"Unified Spatial Intersection Algorithms Based on Conformal Geometric Algebra","volume":"2016","author":"Zhang","year":"2016","journal-title":"Math. Probl. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e1853","DOI":"10.1002\/met.1853","article-title":"Statistical prediction of typhoon-induced accumulated rainfall over the Korean Peninsula based on storm and rainfall data","volume":"27","author":"Kim","year":"2020","journal-title":"Meteorol. Appl."},{"key":"ref_33","unstructured":"Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Liu, Z., Berner, J., Wang, W., Powers, J.G., Duda, M.G., and Barker, D.M. (2019). A Description of the Advanced Research WRF Model Version 4, National Center for Atmospheric Research."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"D10103","DOI":"10.1029\/2010JD015069","article-title":"Impact of intermittent spectral nudging on regional climate simulation using Weather Research and Forecasting model","volume":"116","author":"Cha","year":"2011","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"6057","DOI":"10.1038\/s41598-019-42339-y","article-title":"Prediction of a typhoon track using a generative adversarial network and satellite images","volume":"9","author":"Lee","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"105422","DOI":"10.1016\/j.atmosres.2020.105422","article-title":"Assimilation of GPM Microwave Imager Radiance data with the WRF hybrid 3DEnVar system for the prediction of Typhoon Chan-hom (2015)","volume":"251","author":"Shen","year":"2021","journal-title":"Atmos. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105160","DOI":"10.1016\/j.atmosres.2020.105160","article-title":"Generation and enhancement mechanisms for extreme orographic rainfall associated with Typhoon Morakot (2009) over the Central Mountain Range of Taiwan","volume":"247","author":"Agyakwah","year":"2021","journal-title":"Atmos. Res."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5292\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:55Z","timestamp":1760144455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5292"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,22]]},"references-count":37,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215292"],"URL":"https:\/\/doi.org\/10.3390\/rs14215292","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,22]]}}}