| Title |
Linkage of Hydrological Model and Machine Learning for Real-time Prediction of River Flood |
| Authors |
이재영(Lee, Jae Yeong) ; 김현일(Kim, Hyun Il) ; 한건연(Han, Kun Yeun) |
| DOI |
https://doi.org/10.12652/Ksce.2020.40.3.0303 |
| Keywords |
자료기반 해석;비선형 자기회귀 인공신경망;실시간 홍수예측;하천범람 Data-based analysis;NARX;Real-time flood prediction;River flood |
| Abstract |
The hydrological characteristics of watersheds and hydraulic systems of urban and river floods are highly nonlinear and contain uncertain variables. Therefore, the predicted time series of rainfall-runoff data in flood analysis is not suitable for existing neural networks. To overcome the challenge of prediction, a NARX (Nonlinear Autoregressive Exogenous Model), which is a kind of recurrent dynamic neural network that maximizes the learning ability of a neural network, was applied to forecast a flood in real-time. At the same time, NARX has the characteristics of a time-delay neural network. In this study, a hydrological model was constructed for the Taehwa river basin, and the NARX time-delay parameter was adjusted 10 to 120 minutes. As a result, we found that precise prediction is possible as the time-delay parameter was increased by confirming that the NSE increased from 0.530 to 0.988 and the RMSE decreased from 379.9 ㎥/s to 16.1 ㎥/s. The machine learning technique with NARX will contribute to the accurate prediction of flow rate with an unexpected extreme flood condition. |