- Volume 16 Issue 1
In this paper, we propose an approach that efficiently builds regional hazardous weather prediction models based on past weather data. Doing so requires finding the proper weather attributes that strongly affect hazardous weather for each region, and that requires a large number of experiments to build and test models with different attribute combinations for each kind of hazardous weather in each region. Using our proposed method, we reduce the number of experiments needed to find the correct weather attributes. Compared to the traditional method, our method decreases the number of experiments by about 45%, and the average prediction accuracy for all hazardous weather conditions and regions is 79.61%, which can help forecasters predict hazardous weather. The Korea Meteorological Administration currently uses the prediction models given in this paper.
Attribute selection;Big data;Hazardous weather;Regional prediction;Support vector machine
- L. Al-Matarneh, A. Sheta, S. Bani-Ahmad, J. Alshaer, and I. Al-oqily, "Development of temperature-based weather forecasting models using neural networks and fuzzy logic," International Journal of Multimedia and Ubiquitous Engineering, vol. 9, no. 12, pp. 343-366, 2014. http://dx.doi.org/10.14257/ijmue.2014.9.12.31 https://doi.org/10.14257/ijmue.2014.9.12.31
- E. T. Al-Shammari, M. Amirmojahedi, S. Shamshirband, D. Petkovic, N. T. Pavlovic, and H. Bonakdari, "Estimation of wind turbine wake effect by adaptive neurofuzzy approach," Flow Measurement and Instrumentation, vol. 45, pp. 1-6, 2015. http://dx.doi.org/10.1016/j.flowmeasinst.2015.04.002 https://doi.org/10.1016/j.flowmeasinst.2015.04.002
- S. Al-Yahyai, Y. Charabi, and A. Gastli, "Review of the use of numerical weather prediction (NWP) models for wind energy assessment," Renewable and Sustainable Energy Reviews, vol. 14, no. 9, pp. 3192-3198, 2010. http://dx.doi.org/10.1016/j.rser.2010.07.001 https://doi.org/10.1016/j.rser.2010.07.001
- M. S. K. Awan and M. M. Awais, "Predicting weather events using fuzzy rule based system," Applied Soft Computing, vol. 11, no. 1, pp. 56-63, 2011. http://dx.doi.org/10.1016/j.asoc.2009.10.016 https://doi.org/10.1016/j.asoc.2009.10.016
- F. Babic, P. Bednar, F. Albert, J. Paralic, J. Bartok, and L. Hluchy, "Meteorological phenomena forecast using data mining prediction methods," in Proceedings of Third International Conference (ICCCI 2011), Gdynia, Poland, 2011, pp. 458-467. http://dx.doi.org/10.1007/978-3-642-23935-945
- S. S. Badhiye, P. N. Chatur, and B. V. Wakode, "Temperature and humidity data analysis for future value prediction using clustering technique: an approach," International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 1, pp. 88-91, 2012.
- V. B. Nikam and B. B. Meshram, "Modeling rainfall prediction using data mining method: A Bayesian approach," in Proceedings of 5th International Conference on Computational Intelligence, Modelling and Simulation (CIMSim), Seoul, Korea, 2013, pp. 132-136. http://dx.doi.org/10.1109/CIMSim.2013.29 https://doi.org/10.1109/CIMSim.2013.29
- F. Olaiya and A. B. Adeyemo, "Application of data mining techniques in weather prediction and climate change studies," International Journal of Information Engineering and Electronic Business, vol. 4, no. 1, pp. 51-59, 2012. http://dx.doi.org/10.5815/ijieeb.2012.01.07 https://doi.org/10.5815/ijieeb.2012.01.07
- A. L. Pyayt, I. I. Mokhov, B. Lang, V. V. Krzhizhanovskaya, and R. J. Meijer, "Machine learning methods for environmental monitoring and flood protection," International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 5, no. 6, pp. 549-554, 2011.
- Y. Radhika and M. Shashi, "Atmospheric temperature prediction using support vector machines," International Journal of Computer Theory and Engineering, vol. 1, no. 1, 55-58, http://dx.doi.org/10.7763/IJCTE.2009.V1.9 https://doi.org/10.7763/IJCTE.2009.V1.9
- K. Rasouli, W. W. Hsieh, and A. J. Cannon, "Daily streamflow forecasting by machine learning methods with weather and climate inputs," Journal of Hydrology, vol. 414-415, pp. 284-293, http://dx.doi.org/2012.10.1016/j.jhydrol.2011.10.039 https://doi.org/10.1016/j.jhydrol.2011.10.039
- L. A. S. Romani, A. M. H. Avila, J. Zullo, C. Traina, and A. J. M. Traina, "Mining relevant and extreme patterns on climate time series with CLIPSMiner," Journal of Information and Data Management, vol. 1, no. 2, pp. 245-260, 2010.
- D. P. Solomatine and K. N. Dulal, "Model trees as an alternative to neural networks in rainfall-runoff modelling," Hydrological Sciences Journal, vol. 48, no. 3, pp. 399-411, 2003. http://dx.doi.org/10.1623/hysj.48.3.399.45291 https://doi.org/10.1623/hysj.48.3.399.45291
- E. Tsagalidis and G. Evangelidis, "The effect of training set selection in meteorological data mining," in Proceedings of 14th Panhellenic Conference on Informatics (PCI), Tripoli, Libya, 2010, pp. 61-65. http://dx.doi.org/10.1109/PCI.2010.37 https://doi.org/10.1109/PCI.2010.37
- D. Wang, X. Zhao, and H. Zhang, "Abnormal weather prediction: A new method combining rough set, BP neural network and temporal association rules," Journal of Information & Computational Science, vol. 9, no. 12, pp. 3477-3485, 2012.
- M. Yesilbudak, S. Sagiroglu, and I. Colak, "A new approach to very short term wind speed prediction using k-nearest neighbor classification," Energy Conversion and Management, vol. 69, pp. 77-86, 2013. http://dx.doi.org/10.1016/j.enconman.2013.01.033 https://doi.org/10.1016/j.enconman.2013.01.033
- Z. Zeng, W. W. Hsieh, W. R. Burrows, A. Giles, and A. Shabbar, "Surface wind speed prediction in the canadian arctic using non-linear machine learning methods," Atmosphere-Ocean, vol. 49, no. 1, pp. 22-31, 2011. http://dx.doi.org/10.1080/07055900.2010.549102 https://doi.org/10.1080/07055900.2010.549102
- G. P. Zhang, "Time series forecasting using a hybrid ARIMA and neural network model," Neurocomputing, vol. 50, pp. 159-175, 2003. http://dx.doi.org/10.1016/S0925-2312(01)00702-0 https://doi.org/10.1016/S0925-2312(01)00702-0
- X. Zhu, J. Cao, and Y. Dai, "A decision tree model for meteorological disasters grade evaluation of flood," in Proceedings of 4th International Joint Conference on Computational Sciences and Optimization (CSO), Yunnan, China, 2011, pp. 916-919. http://dx.doi.org/10.1016/10.1109/CSO.2011.26
- J. Lee, S. Hong, and J. H. Lee, "An efficient prediction for heavy rain from big weather data using genetic algorithm," in Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication (ICUIMC'14), Siem Reap, Cambodia, 2014. http://dx.doi.org/10.1145/2557977.2558048 https://doi.org/10.1145/2557977.2558048
- S. Fan, L. Chen, and W. J. Lee, "Short-term load forecasting using comprehensive combination based on multimeteorological information," IEEE Transactions on Industry Applications, vol. vol. 45, no. 4, pp. 1460-1466, 2009. http://dx.doi.org/10.1109/TIA.2009.2023571 https://doi.org/10.1109/TIA.2009.2023571
- A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, "Current methods and advances in forecasting of wind power generation," Renewable Energy, vol. 37, no. 1, pp. 1-8, 2012. http://dx.doi.org/10.1016/j.renene.2011.05.033 https://doi.org/10.1016/j.renene.2011.05.033
- L. Ingsrisawang, S. Ingsriswang, S. Somchit, P. Aungsuratana, and W. Khantiyanan, "Machine learning techniques for short-term rain forecasting system in the northeastern part of Thailand," International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 2, no. 5, pp. 1422-1427, 2008.
- K. Napierala and J. Stefanowski, "BRACID: a comprehensive approach to learning rules from imbalanced data," Journal of Intelligent Information Systems, vol. 39, no. 2, pp. 335-373, 2012. http://dx.doi.org/10.1007/s10844-011-0193-0 https://doi.org/10.1007/s10844-011-0193-0
- R. Nayak, P. S. Patheja, and A. Waoo, "An enhanced approach for weather forecasting using neural network," in Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS2011), Roorkee, India, 2011, pp. 833-839. http://dx.doi.org/10.1007/978-81-322-0491-676
- A novel approach for optimizing climate features and network parameters in rainfall forecasting pp.1433-7479, 2017, https://doi.org/10.5391/IJFIS.2016.16.1.1
연구 과제번호 : Developing On-line Open Platform to Provide Local-business Strategy Analysis and User-targeting Visual Advertisement Materials for Micro-enterprise Managers
연구 과제 주관 기관 : IITP, National Research Foundation of Korea (NRF)