| Title |
Application and Comparison of Dynamic Artificial Neural Networks for Urban Inundation Analysis |
| Authors |
김현일(Kim, Hyun Il) ; 금호준(Keum, Ho Jun) ; 한건연(Han, Kun Yeun) |
| DOI |
https://doi.org/10.12652/Ksce.2018.38.5.0671 |
| Keywords |
동적 신경망;자료지향형 모델;오차분석;도시홍수 Dynamic neural network;Data-driven model;Error analysis;Urban flood |
| Abstract |
The flood damage caused by heavy rains in urban watershed is increasing, and, as evidenced by many previous studies, urban flooding usually exceeds the water capacity of drainage networks. The flood on the area which considerably urbanized and densely populated cause serious social and economic damage. To solve this problem, deterministic and probabilistic studies have been conducted for the prediction flooding in urban areas. However, it is insufficient to obtain lead times and to derive the prediction results for the flood volume in a short period of time. In this study, IDNN, TDNN and NARX were compared for real-time flood prediction based on urban runoff analysis to present the optimal real-time urban flood prediction technique. As a result of the flood prediction with rainfall event of 2010 and 2011 in Gangnam area, the Nash efficiency coefficient of the input delay artificial neural network, the time delay neural network and nonlinear autoregressive network with exogenous inputs are 0.86, 0.92, 0.99 and 0.53, 0.41, 0.98 respectively. Comparing with the result of the error analysis on the predicted result, it is revealed that the use of nonlinear autoregressive network with exogenous inputs must be appropriate for the establishment of urban flood response system in the future. |