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A tropical cyclone disaster is one of the most destructive natural hazards on earth and the main cause of death or injury to humans as well as damage or loss of valuable goods or properties, such as buildings, communication systems, agricultural land, economic losses, etc. To mitigate catastrophic phenomenon, a Modern Natural Disaster Management model or MNDM has been formulated and the most important phase in MNDM is the emphasis on the process before the catastrophic phenomenon or preparing tropical cyclone track forecasting, intensity forecasting, and risk area identification. Although Tropical Cyclone (TC) track and intensity forecasting has been steadily improving over the decades but some uncertainties still remain, a part of them is due to an inherent predictability bound that future improvement in the numerical model and most forecasting techniques will not be able to overcome. Moreover, risk area assessment and uncertainty of the major model, which is the most important phase in MNDM is excluded. To address these problems, this paper proposes an integrated short-range tropical cyclone hazardous area forecasting system that includes track, intensity and hazardous area forecasting in the system by using 13 features that are extracted from satellite images with the improvement of the traditional statistical methods. In addition, the model can display a graphic image of the geologically hazardous area by using three classes of intensity impact level i.e., using R34, R50 and R64, which are the radius of the maximum wind speed at each level for bounding area. The performance of the model was satisfactory, the average error from experiment results of R34, R50 and R64 forecasting with unknown tropical cyclone data between years 2013–2015 on Mercator projection map were lower than traditional techniques by 32.31%, 23.72% and 26.18% respectively.
 A. Buranasing and A. Prayote, “Tropical storm disaster management in Thailand,” presented at the The National Geo-Informatics and Space Technology Conference (GEOINFOTECH 2014), Bangkok, Thailand, Nov. 12–14, 2014.
 M. Miller. (2009, Nov.). Global Tropical Cyclone Forecasting for Days, Weeks and a Season Ahead. The European Centre for Medium-Range Weather Forecasts – ECMWF, Reading, United Kingdom [Online]. Available: https://www.ecmwf.int/
 A. Buranasing and A. Prayote, “Storm intensity estimation using symbolic aggregate approximation and artificial neural network,” in Proceedings The 18th International Computer Science and Engineering Conference (ICSEC 2014: International Track), 2014, pp. 249–252.
 Thai Meteorological Department. (2014, May). Weather Forecasting. Thai Meteorological Department. Bangkok, Thailand [Online]. Available: http://www.tmd.go.th/en
 S. Thamsaroch. (1991). Natural Disaster in Thailand. Thai Meteorological Department, Bangkok, Thailand [Online]. Available: https://www.tmd.go.th/en/ (in Thai)
 R. Kovordányi and C. Roy, “Cyclone track forecasting based on satellite images using artificial neural networks,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 64, no. 6, Nov. 2009.
 B. Kongaed and D. Sukawat, “A parametric tropical cyclone wind model for Thailand,” M.S. thesis, Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology Thonburi, 2005.
 Meteorological Development Bureau. (2010). Tropical Cyclones in Thailand Historical Data 1951–2010. Thai Meteorological Department, Bangkok, Thailand [Online]. Available: https://www.tmd.go.th/en/ (in Thai)
 Typhoon Harriet. (2015, July). 1962 Pacific Typhoon Season. Wikipedia. US [Online]. Available:https://en.wikipedia.org/wiki/1962_Pacific_typhoon_season#Tropical_Storm_Harriet
 Typhoon Gay. (2015, July). Typhoon Gay (1989). Wikipedia. US [Online]. Available: https://en.wikipedia.org/wiki/Typhoon_Gay_(1989)
 Typhoon Linda. (2015, July). Tropical Storm Linda (1997). Wikipedia. US [Online]. Available:https://en.wikipedia.org/wiki/Tropical_Storm_Linda_(1997)
 D. P. Coppola, Introduction to International Disaster Management. New York, US: Elsevier’s Science & Technology, 2015.
 M. Pruksapong. (2013, Jun.). Disaster risk reduction and development. Asian Disaster Preparedness Center (ADPC), Bangkok, Thailand [Online]. Available: https://www.adpc .net/igo/?
 R. Pasch and J. S. Clark. (2009). Technical Summary of the National Hurricane Center Track and Intensity Models. National Oceanic and Atmospheric Administration (NOAA), Maryland, U.S. [Online]. Available: https://www.noaa.gov/
 P. Jampanya. (2013, Jun.). Tropical storm forecasting technique in Thailand. Weather Forecast Bureau, Bangkok, Thailand [Online]. Available:http://www.apectyphoon.org/sdt175/img/img/3859/Session%20II/5.TS%20Forecasting.pdf
 UCAR. (2015, July). Weather Research and Forecasting Model. UCAR. Colorado, U.S. [Online]. Available: http://www.wrf-model.org
 S. Yavinchan, Tropical storm track prediction for Thailand by a high resolution numerical weather prediction model. Bangkok, Thailand: The Meteorological Department, Weather Forecast Bureau, 2015.
 A. Buranasing and A. Prayote, “Application of remote sensing for tropical cyclone track forecasting based on statistical methods,” in Proceedings of the 19th International Computer Science and Engineering Conference (ICSEC 2015), 2015, pp. 187–192.
 A. Buranasing and A. Prayote, “Tropical cyclone track and intensity forecasting using remotely-sensed images,” in Proceedings of the 7th International Conference on Information Technology and Electrical Engineering (ICITEE 2015), 2015, pp. 626–631.
 Thai Meteorological Department, “The Climate of Thailand,” Thai Meteorological Department, Bangkok, Thailand, Rep. 551.582-02-1994, 2010 (in Thai).
 Naval Meteorology and Oceanography Command. (2015, July). Joint Typhoon Warning Center (JTWC). Naval Meteorology and Oceanography Command, U. S. [Online]. Available: http://www.usno.navy.mil/JTWC/
 Japan Meteorological Agency (JMA). (2015, July). Meteorological Satellites. Japan Meteorological Agency (JMA), Tokyo, Japan [Online].Available:http://www.jma.go.jp/jma/jma-eng/satellite/index.html
 Japan Meteorological Agency (JMA). (2015, July). The history of meteorological satellites at JMA. Japan Meteorological Agency (JMA), Tokyo, Japan [Online]. Available: http://www.jma.go.jp/jma/jma-eng/satellite/introduction/history.html
 R. R. Kelkar, Satellite meteorology. Andhra Pradesh, India: BS Publications, 2007.
 P. M. Mather and M. Koch, Computer Processing of Remotely-Sensed Images, 4th ed. Hoboken, New Jersey: John Wiley & Sons, Ltd., 2011.
 J. Kaňák, “Overview of the IR channels and their applications,” EUMeTrain, Vienna, Austria, Jun. 2011.
 M. setvak, “Enhancement of the IR imagery,” EUMeTrain, Vienna, Austria, Jun. 2011.
 N. Jaiswal and C. M. Kishtawal, “Automatic determination of center of tropical cyclone in satellite-generated IR images,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 3, pp. 460–463, 2011.
 N. Jaiswal and C. M. Kishtawal. “Objective detection of center of tropical cyclone in remotely sensed infrared images,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 1031–1035, 2013.
 Q. P. Zhang, L. L. Lai, and W. C. Sun, “Intelligent location of tropical cyclone center,” in Proceedings of The Fourth International Conference on Machine Learning and Cybernetics, 2005, pp. 423–428.
 W. K. Yan, Y. C. Lap, L. P. Wah, and T. W. Wan, “Automatic template matching method for tropical cyclone eye fix,” presented at the 17th International Conference on Pattern Recognition (ICPR’04), Cambridge, UK, Aug. 26, 2004.
 T. L. Olander and C. S. Velden. “The advanced dvorak technique: Continued development of an objective scheme to estimate tropical cyclone intensity using geostationary infrared satellite imagery,” American Meteorological Society, vol. 22, pp. 287–298, 2007.
 Y. Xu and C. J. Neumann, A Statistical Model for the Prediction of Western North Pacific Tropical Cyclone Motion. Miami, Florida: National Hurricane Center, 1985.
 G. W. Collins, Fundamental Numerical Methods and Data Analysis. US: The NASA Astrophysics Data System, 2003.
 T. Dupont, M. Plu, P. Caroff, and G. Faure, “Verification of ensemble-based uncertainty circles around tropical cyclone track forecasts,” Weather and Forecasting, American Meteorological Society, vol. 26, pp. 664–676, 2011.
 T. Dupont, M. Plu, P. Caroff, and G. Faure, “Operational implementation of ensemble-based dynamical uncertainty circdles around tropical cyclone track forecasts,” in Proceedings The 30th Conference on Hurricanes and Tropical Meteorology, 2012.
 Center for Educational Technologies. (2014, May). The Cone of Uncertainty. Center for Educational Technologies. Washington, DC, US [Online] Available: http://www.cet.edu/
 Naval Oceanography Portal. (2015, Jul.). Tropical Cyclone Quick Reference Guide 2015. Naval Oceanography Portal. Washington, DC, US [Online] Available: http://www.usno.navy.mil/