한국수자원학회:학술대회논문집 (Proceedings of the Korea Water Resources Association Conference)
- 한국수자원학회 2020년도 학술발표회
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- Pages.183-183
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- 2020
Radar Quantitative Precipitation Estimation using Long Short-Term Memory Networks
- Thi, Linh Dinh (Dept. of Civil and Environ. Eng., Sejong University) ;
- Yoon, Seong-Sim (Hydro Science and Engineering Research Institute, Korea Institute of Civil Engineering and Building technology) ;
- Bae, Deg-Hyo (Dept. of Civil and Environ. Eng., Sejong University)
- 발행 : 2020.06.24
초록
Accurate quantitative precipitation estimation plays an important role in hydrological modelling and prediction. Instantaneous quantitative precipitation estimation (QPE) by utilizing the weather radar data is a great applicability for operational hydrology in a catchment. Previously, regression technique performed between reflectivity (Z) and rain intensity (R) is used commonly to obtain radar QPEs. A novel, recent approaching method which might be applied in hydrological area for QPE is Long Short-Term Memory (LSTM) Networks. LSTM networks is a development and evolution of Recurrent Neuron Networks (RNNs) method that overcomes the limited memory capacity of RNNs and allows learning of long-term input-output dependencies. The advantages of LSTM compare to RNN technique is proven by previous works. In this study, LSTM networks is used to estimate the quantitative precipitation from weather radar for an urban catchment in South Korea. Radar information and rain-gauge data are used to evaluate and verify the estimation. The estimation results figure out that LSTM approaching method shows the accuracy and outperformance compared to Z-R relationship method. This study gives us the high potential of LSTM and its applications in urban hydrology.
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