NDLI: A self-tuning ANN model for simulation and forecasting of surface flows

Content Provider	Springer Nature Link
Author	Bozorg Haddad, Omid Zarezadeh Mehrizi, Mahboubeh Abdi Dehkordi, Mehri Loáiciga, Hugo A. Mariño, Miguel A.
Copyright Year	2016
Abstract	Artificial neural networks (ANN) are applicable for and forecasting without the need to calculate complex nonlinear functions. This paper evaluates the effectiveness of temperature, evapotranspiration, precipitation and inflow factors, and the lag time of those factors, as variables for simulating and forecasting of runoff. The genetic algorithm (GA) is coupled with ANN to determine the optimal set of variables for streamflow forecasting. The minimization of the total mean square error (MSE) is considered as the objective function of the ANN-GA method in this paper. Our results show the effectiveness of the ANN-GA for simulating and forecasting runoff with consistent accuracy compared with using pure ANN for runoff simulation and forecasting.
Starting Page	2907
Ending Page	2929
Page Count	23
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	30
Issue Number	9
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2016-04-19
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural network Runoff parameters Simulation and forecasting Effective factors Optimization Genetic algorithm Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

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