| Title |
Design of Echo Classifier Based on Neuro-Fuzzy Algorithm Using Meteorological Radar Data |
| Authors |
오성권(Oh, Sung-Kwun) ; 고준현(Ko, Jun-Hyun) |
| DOI |
https://doi.org/10.5370/KIEE.2014.63.5.676 |
| Keywords |
RBFNNs(Radial Basis Function Neural Networks) ; FCM(Fuzzy C-means Method) ; Precipitation echo(PRE) ; Non-precipitaion echo(N-PRE) ; Echo judgment module ; Pattern classifier ; Radar data |
| Abstract |
In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance. |