Abstract
For recent several years, we have suffered from a variety of unusual weather phenomena. In particular, regional torrential rains have caused an immeasurable losses of life and property, and the forecast of heavy rainfall becomes progressively important as time goes on. We study wrapper-based genetic feature selection using machine learning techniques such as SVM or k-NN for very short-term heavy rainfall prediction in the southern part of the Korean Peninsula. Historical weather data were collected from 408 AWSes of the Korea Meteorological Administration during recent 4 years. The data from 2007 to 2008 were selected to train SVM and k-NN models and the data of the year 2009 were used as a validation set in our genetic algorithm, and the data of the year 2010 were used as a test set. Undersampling is to match the number of samples of a high frequency with that of a low frequency. We undersampled the train set into two classes: heavy rainfall (more than 70mm for 6 hours or more than 110mm for 12 hours) and the other. Test without undersampling produced low ETS and took too long time. The validation set was used to choose the important ones among 72 features using a genetic algorithm. Normalized data between 0 and 1 had a good influence on the performance compared to the test without normalization, and especially on SVM. The SVM using important features performed about 3.5 times better than that using all features.
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Seo, JH., Kim, YH. (2012). Genetic Feature Selection for Very Short-Term Heavy Rainfall Prediction. In: Lee, G., Howard, D., Kang, J.J., Ślęzak, D. (eds) Convergence and Hybrid Information Technology. ICHIT 2012. Lecture Notes in Computer Science, vol 7425. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32645-5_40
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DOI: https://doi.org/10.1007/978-3-642-32645-5_40
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