Abstract
River flow estimation using records of past time series is importance in water resources engineering and management and is required in hydrologic studies. In the past two decades, the approaches based on the artificial neural networks (ANN) were developed. River flow modeling is a non-linear process and highly affected by the inputs to the modeling. In this study, the best input combination of the models was identified using the Gamma test then MLP–ANN and hybrid multilayer perceptron (MLP–FFA) is used to forecast monthly river flow for a set of time intervals using observed data. The measurements from three gauge at Ajichay watershed, East Azerbaijani, were used to train and test the models approach for the period from January 2004 to July 2016. Calibration and validation were performed within the same period for MLP–ANN and MLP–FFA models after the preparation of the required data. Statistics, the root mean square error and determination coefficient, are used to verify outputs from MLP–ANN to MLP–FFA models. The results show that MLP–FFA model is satisfactory for monthly river flow simulation in study area.
Highlights
River flow simulation is significant for planning and management of catchment area, evaluation of risk and control of droughts, floods, development of water resources, production of hydroelectric energy, navigation planning and allocation of water for agriculture (Khatibi et al 2012).Simulation of river flow is great importance for protection and simulation of changes in marine ecosystems
It can be found that the developed MLP–FFA3 model out performs the artificial neural networks (ANN) model developed in this research for simulation monthly river flow and is sufficient for modeling river flow
Multilayer perceptron artificial neural networks (MLP–ANN) and MLP–FFA models were employed for modeling river flow using monthly data
Summary
River flow simulation is significant for planning and management of catchment area, evaluation of risk and control of droughts, floods, development of water resources, production of hydroelectric energy, navigation planning and allocation of water for agriculture (Khatibi et al 2012).Simulation of river flow is great importance for protection and simulation of changes in marine ecosystems. Different methods are used for river flow simulation including time series analysis, fuzzy logic, neurofuzzy, genetic programming, artificial neural networks and recently, chaos theory. ANNs applied by Anmala et al (2000) for river flow estimation in three watersheds in Kansas. Wu et al (2005) developed the using of ANNs for watershed runoff and river flow simulations. Back propagation (This technique is sometimes called backward propagation of errors) ANN, runoff models applied by Sarkar et al (2006) to estimate and prediction daily runoff for a part of the Satluj river basin of India. Comparison of different ANN models applied by Kisi (2007) for short term daily river flow estimation. Kalteh (2008) applied ANNs model for the estimation of streamflow and used Garson’s algorithm for determining the relative significant of inputs, neural interpretation diagram, and randomization approach. Dorum et al (2010) studied to set up rainfall–runoff
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