A dynamic artificial neural network for assessment of land-use change impact on warning lead-time of flood

Floods always require innovative models for flood forecasting. This paper proposes a dynamic artificial neural network (DANN) model for evaluating land-use change impact (LUCI) scenarios on weighted average of warning lead-time of flood (WAWLTF) in an urbanised watershed. The simulated floods of a calibrated HEC-HMS hydrological model were used for training and testing of DANN model. The features of proposed DANN's structure were determined by minimisation of a new flood forecasting error (FFE) index. Results showed that the proposed procedure was able to optimise features of DANN structure by minimising FFE and produced an appropriate DANN model for assessment of LUCI on WAWLTF. The results also denoted that practicing suitable watershed management in future may improve WAWLTF encouragingly but never compensates negative impact of urbanisation completely. In conclusion, the model can be used as an efficient tool in similar urbanised watershed for assessment of LUCI on WAWLTF.

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