Abstract
In many countries, floods are the leading natural disaster in terms of damage and losses per year. Early prediction of such events can help prevent some of those losses. Artificial neural networks (ANN) show a strong ability to deal quickly with large amounts of measured data. In this work, we develop an ANN for outputting flood inundation maps based on multiple discharge inputs with a high grid resolution (4 m × 4 m). After testing different neural network training algorithms and network structures, we found resilience backpropagation to perform best. Furthermore, by introducing clustering for preprocessing discharge curves before training, the quality of the prediction could be improved. Synthetic flood events are used for the training and validation of the ANN. Historical events were additionally used for further validation with real data. The results show that the developed ANN is capable of predicting the maximum flood inundation extents. The mean squared error in more than 98 and 86% of the total area is smaller than 0.2 m2 in the prediction of synthetic events and historical events, respectively.
Highlights
Flood is one of the most damaging natural hazards hitting settlements which threatens the safety of civilians and the integrity of infrastructures (Berz, 2001)
Two training algorithms are applied for training the Artificial neural networks (ANN) model using the same training dataset (Event #1–#120): resilient backpropagation (RP) and the conjugate gradient (CGF)
Both generated models are evaluated using the mean squared error (MSE) over the remaining runs (60) in the testing dataset (Event #121– #180)
Summary
Flood is one of the most damaging natural hazards hitting settlements which threatens the safety of civilians and the integrity of infrastructures (Berz, 2001). Flooding is the leading cause of damage and losses in many countries in the world (Kron, 2005). One-dimensional (1D) drainage model solving the one-dimensional Saint-Venant flow equations, can be applied for simulating the surcharge or drainage of the underground drainage network (Mark et al, 2004). The two-dimensional (2D) Saint-Venant flow equations are ideal tools for simulating the urban surface inundation, and obtain the maximum flood extents, maximum depths and flow velocity on many points on the surface. The 1D–2D coupling model simulates the drainage network and the urban surface simultaneously.
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