References
- McCloskey, D. N. "The art of forecasting: From ancient to modern times." Cato J. 12, pp.23-43, 1993.
- Zschau, J., and Kuppers, A. N. eds. Early Warning Systems for Natural Disaster Reduction. Springer Science & Business Media, 2013.
- Xie, P., and Arkin, P. A. "Analyses of global monthly precipitation using gauge observations, satellite estimates, and numerical model predictions." Journal of climate Vol. 9, No. 4, pp.840-858, 1996. https://doi.org/10.1175/1520-0442(1996)009<0840:AOGMPU>2.0.CO;2
- Adeli, H., and Panakkat, A. "A probabilistic neural network for earthquake magnitude prediction." Neural networks Vol. 22, No. 7, pp.1018-1024, 2009. https://doi.org/10.1016/j.neunet.2009.05.003
- H. H. Lee, S. H. Chung, E. J. Choi, "A Case Study on Machine Learning Applications and Performance Improvement in Learning Algorithm", Journal of Digital Convergence, Vol. 14, No. 2, pp.245-258, 2016. https://doi.org/10.14400/JDC.2016.14.2.245
- Y. D. Yun, Y. Wook. Yang, H. S. Ji, H. S. Lim, "Development of Smart Senior Classification Model based on Activity Profile Using Machine Learning Method", Journal of the Korea Convergence Society, Vol. 8, No. 1, pp.25-34, 2017. https://doi.org/10.15207/JKCS.2017.8.1.025
- Matkan, A. A., M. Hajeb, and Z. Azarakhsh. "Oil spill detection from SAR image using SVM based classification." ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Vol. 1. No. 3, pp.55-60, 2013.
- Tagliaferri, F., I. M. Viola, and R . G. J. Flay. "Wind direction forecasting with artificial neural networks and support vector machines." Ocean Engineering 97, pp.65-73, 2015. https://doi.org/10.1016/j.oceaneng.2014.12.026
- Chang, C. C., and Lin, C. J. "LIBSVM: a library for support vector machines." ACM Transactions on Intelligent Systems and Technology (TIST) Vol. 2, No. 3, pp.27, 2011.
- Zhao, P, Xia, J., Dai, Y., and He, J. "Wind speed prediction using support vector regression." 5th IEEE Conference on Industrial Electronics and Applications. IEEE, 2010.
- Yoo, C. S., and Park, J. Y. "Combining radar and rain gauge observations utilizing Gaussian-process-based regression and support vector learning." Journal of Korean Institute of Intelligent Systems Vol. 18, No. 3, pp.297-305, 2008. https://doi.org/10.5391/JKIIS.2008.18.3.297
- Petelin, D., Mlakar, P., Boznar, M. Z., Grasic, B. and Kocijan, J. "Ozone forecasting using Gaussian processes and perceptron neural networks", 16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes 2014.
- Liu, J. N., Hu, Y., He, Y., Chan, P. W. and Lai, L. "Deep neural network modeling for big data weather forecasting." Information Granularity, Big Data, and Computational Intelligence. Springer International Publishing, pp.389-408, 2015.
- Dalto, M., Vasak, M., Baotic, M., Matusko, J., and Horvath, K. "Neural-network-based ultra-short-term wind forecasting." European Wind Energy Association 2014 Annual Event 2014.
- Yang, L., Tian, S., Yu, L., Ye, F., Qian, J., and Qian, Y. "Deep learning for extracting water body from Landsat imagery." International Journal of Innovative Computing, Information and Control Vol. 11, No. 6, 2015.
- Baruque, B., Corchado, E., Mata, A., and Corchado, J. M. "forecasting solution to the oil spill problem based on a hybrid intelligent system." Information Sciences Vol. 180, No.10, pp.2029-2043, 2010. https://doi.org/10.1016/j.ins.2009.12.032
- Topouzelis, K., Karathanassi, V., Pavlakis, P., and Rokos, D. "Potentiality of feed -forward neural networks for classifying dark formations to oil spills and look-alikes." Geocarto International Vol. 24, No. 3, pp.179-191, 2009. https://doi.org/10.1080/10106040802488526
- Ramedani, Z., Omid, M., Keyhani, A., Shamshirband, S. and Khoshnevisan, B. "Potential of radial basis function based support vector regression for global solar radiation prediction." Renewable and Sustainable Energy Reviews 39, pp.1005-1011, 2014. https://doi.org/10.1016/j.rser.2014.07.108