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

Authors: Mohammad Ebrahim Banihabib; Azar Arabi; Ali A. Salha

Addresses: Department of Irrigation and Drainage Engineering, University College of Abureyhan, University of Tehran, P.O. Box 3391653755, Tehran, Iran ' Department of Irrigation and Drainage Engineering, University College of Abureyhan, University of Tehran, P.O. Box 3391653755, Tehran, Iran ' Utah Water Research Laboratory, Utah State University, Logan, USA

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.

Keywords: 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.

DOI: 10.1504/IJHST.2015.070093

International Journal of Hydrology Science and Technology, 2015 Vol.5 No.2, pp.163 - 178

Received: 02 Sep 2014
Accepted: 01 Feb 2015
Published online: 26 Jun 2015 *

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