TY - GEN
T1 - Feature selection for heavy rain prediction using genetic algorithms
AU - Lee, Jaedong
AU - Kim, Jaekwang
AU - Lee, Jee Hyong
AU - Cho, Ik Hyun
AU - Lee, Jeong Whan
AU - Park, Kyoung Hee
AU - Park, Jeonggyun
PY - 2012
Y1 - 2012
N2 - ECMWF (European Centere of Medium-Range Weather Forecasts) produces weather data every six hours. In the case of ECMWF 1.125 degree weather data, the northern hemisphere is divided into 320×161 grids and each grid has 254 weather features. Since we are aim to forecast heavy rain in the Korea Peninsula, we need only 10×10 grids around the Korean Peninsula. However, the number of inputs to the forecasting system will be 100 dimensions (10×10) even if we consider only one weather feature. If we consider 3 features, it is 300 dimensions (10×10×3). Therefore, as more features are combined, the size of the data is increased and it causes the computational cost high. In order to reduce the size of inputs to the forecasting system, we apply genetic algorithms for the feature selection in this paper. As a result, it has been found out that it is possible to assort with a higher accuracy rate with a smaller data set.
AB - ECMWF (European Centere of Medium-Range Weather Forecasts) produces weather data every six hours. In the case of ECMWF 1.125 degree weather data, the northern hemisphere is divided into 320×161 grids and each grid has 254 weather features. Since we are aim to forecast heavy rain in the Korea Peninsula, we need only 10×10 grids around the Korean Peninsula. However, the number of inputs to the forecasting system will be 100 dimensions (10×10) even if we consider only one weather feature. If we consider 3 features, it is 300 dimensions (10×10×3). Therefore, as more features are combined, the size of the data is increased and it causes the computational cost high. In order to reduce the size of inputs to the forecasting system, we apply genetic algorithms for the feature selection in this paper. As a result, it has been found out that it is possible to assort with a higher accuracy rate with a smaller data set.
KW - Big Data Mining
KW - Genetic Algorithm
KW - Heavy Rain Prediction
KW - Support Vector Machine
UR - https://www.scopus.com/pages/publications/84877793802
U2 - 10.1109/SCIS-ISIS.2012.6505383
DO - 10.1109/SCIS-ISIS.2012.6505383
M3 - Conference contribution
AN - SCOPUS:84877793802
SN - 9781467327428
T3 - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
SP - 830
EP - 833
BT - 6th International Conference on Soft Computing and Intelligent Systems, and 13th International Symposium on Advanced Intelligence Systems, SCIS/ISIS 2012
T2 - 2012 Joint 6th International Conference on Soft Computing and Intelligent Systems, SCIS 2012 and 13th International Symposium on Advanced Intelligence Systems, ISIS 2012
Y2 - 20 November 2012 through 24 November 2012
ER -