A dynamic artificial neural network for assessment of land-use change impact on warning lead-time of flood. (2015)

• Record Type: Journal Article
• Authors: Banihabib, Mohammad Ebrahim; Arabi, Azar; Salha, Ali A.
• Is Part Of: International journal of hydrology science and technology. Volume 5:Number 2(2015)
• Page Start: 163
• Page End: 178
• Publication Date: 2015
• Subjects: dynamic ANNs -- artificial neural networks -- DANN -- land use change -- change impact -- forecast lead times -- watershed management -- Tajrish -- flood control -- flood warnings -- Iran -- Tehran -- warning lead times -- flooding -- flood forecasting -- urban watersheds -- simulation -- hydrological modelling -- urbanisation; Hydrology -- Periodicals; Water resources development -- Periodicals; Hydrology; 553.705
• Languages: English
• ISSNs: 2042-7808

Abstract

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.