Daily rainfall-runoff modelling by neural networks in semi-arid zone: case of Wadi Ouahrane’s basin

Abstract

This research work will allow checking efficiency of formal neural networks for flows' modelling of wadi Ouahrane's basin from rainfall-runoff relation which is non-linear. Two models of neural networks were optimized through supervised learning and compared in order to achieve this goal, the first model with input rain, and the second one with rain and input ETP. These neuronal models were compared with another overall model, the GR4j model. Then, it has been optimized and compared with the three first models, a third model of neural network with rain, ETP and soil moisture (calculated by the model GR4j) input.The neuronal models were optimized with algorithm of Levenberg Marquarld (LM), while the GR4j model was optimized with SCE-UA method. The Nash criterion (%) and the correlation coefficient of Pearson (R) allowed appreciating performances of these models.

Journal of Water and Land Development

“…the black box or empirical models. The application of black box models based on the ANN technique has become increasingly popular in hydrology and water management due to their ability and potential to provide satisfactory modeling of the intricate rainfall-runoff mechanism under limited data availability [AICHOURI et al 2015;BENZINEB, REMAOUN 2016;MACHADO et al 2011;SRINIVASULU, JAIN 2006;YASEEN 2015]. BHADRA et al [2010] and REZAEIANZADEH et al [2013] have compared the performance of different conceptual and ANN techniques for modeling and reported that the ANN approach which does not have physically realistic components and parameters, outperformed conventional conceptual models.…”