발간년도 : [2017]
| 논문정보 |
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| 논문명(한글) |
[Vol.12, No.2] A Study on Rainfall Estimation of High Resolution Using Machine Learning |
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| 논문투고자 |
Min-Gyu Kim, Moo-Hun Lee |
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| 논문내용 |
Occurred rainfall as local torrential rainfalls among the climate change has a decisive effect on life. Rainfall is closely related to the living of people, its exact prediction is important to us. In this paper, we have improved the estimation accuracy of rainfall by comparing the applicable interpolation algorithms of meteorological field. The comparison algorithms are Kriging, MLP(Multi-Layer Perceptron), SVR(Support Vector Regression), Random Forest and Bagging. For the experiment, we used the AWS(Automatic Weather System) data which exists the event of rain on August 16th, 2015, 16:00~20:00. The AWS data is collected by a 1-minute cycle, the attributes related to rainfall include precipitation type, accumulated rainfall such as 10 minutes, 15 minutes, 30 minutes, and 1 day, etc. The dataset consists of training data and test data in 255 observation stations, Seoul. 230 stations are used as training data, the remaining stations are used as test data. Validation methods such as RMSE, R-square, Correlation Coefficient, CSI(Critical success index) and BIAS(frequency BIAS) were used to compare the algorithms. CSI and BIAS were measured for precision of rainfall and calculated using the rain contingency table. Our experimental results showed that bagging algorithm performed better than the others in estimating the rainfall information. |
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