Artificial neural network for modelling rainfall-runoff

The use of an artificial neural network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature and therefore, representing their physical characteristics is challenging. In this research, ANN modelling is developed with the use of the MATLAB toolbox for predicting river stream flow coming into the Ringlet reservoir in Cameron Highland, Malaysia. A back propagation algorithm is used to train the ANN. The results indicate that the artificial neural network is a powerful tool in modelling rainfall-runoff. The obtained results could help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

[1]  R Govindaraju,et al.  ARTIFICIAL NEURAL NETWORKS IN HYDROLOGY: II, HYDROLOGIC APPLICATIONS , 2000 .

[2]  Luis S. Pereira,et al.  Estimation of ETo with Hargreaves–Samani and FAO-PM temperature methods for a wide range of climates in Iran , 2013 .

[3]  A. Akbarzadeh,et al.  Development of pedo transfer functions (PTFs) to predict soil physico-chemical and hydrological characteristics in southern coastal zones of the Caspian Sea , 2009 .

[4]  K. P. Sudheer,et al.  Explaining the internal behaviour of artificial neural network river flow models , 2004 .

[5]  Hikmet Kerem Cigizoglu,et al.  Comparison of Rainfall-Runoff Relationship Modeling using Different Methods in a Forested Watershed , 2015, Water Resources Management.

[6]  P. C. Nayak,et al.  Rainfall-runoff modeling using conceptual, data driven, and wavelet based computing approach , 2013 .

[7]  Selva Balan,et al.  Artificial Neural Network based Runoff Prediction Model for a Reservoir , 2012 .

[8]  Özgür Kisi,et al.  Modeling rainfall-runoff process using soft computing techniques , 2013, Comput. Geosci..

[9]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[10]  Abdolreza Bahremand,et al.  Predictive Analysis and Simulation Uncertainty of a Distributed Hydrological Model , 2010 .

[11]  Souad Riad,et al.  Rainfall-runoff model usingan artificial neural network approach , 2004, Math. Comput. Model..

[12]  Ashu Jain,et al.  Development of effective and efficient rainfall‐runoff models using integration of deterministic, real‐coded genetic algorithms and artificial neural network techniques , 2004 .

[13]  Yousry Mahmoud Ghazaw,et al.  Runoff forecasting by artificial neural network and conventional model , 2011 .

[14]  Carlos Alberto Brayner de Oliveira Lira,et al.  Calibration of Hargreaves-Samani Equation for Estimating Reference Evapotranspiration in Sub-Humid Region of Brazil , 2013 .

[15]  C. L. Wu,et al.  Rainfall–runoff modeling using artificial neural network coupled with singular spectrum analysis , 2011 .

[16]  Muhammad Mukhlisin,et al.  Performance of artificial neural network and regression techniques for rainfall-runoff prediction , 2011 .

[17]  Ricardo Colomo Palacios,et al.  CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator , 2011, Expert Syst. Appl..

[18]  Tawatchai Tingsanchali,et al.  Application of tank, NAM, ARMA and neural network models to flood forecasting , 2000 .

[19]  C. Young,et al.  Prediction and modelling of rainfall–runoff during typhoon events using a physically-based and artificial neural network hybrid model , 2015 .

[20]  Ashu Jain,et al.  A comparative analysis of training methods for artificial neural network rainfall-runoff models , 2006, Appl. Soft Comput..

[21]  Rao S. Govindaraju,et al.  Prediction of watershed runoff using Bayesian concepts and modular neural networks , 2000 .

[22]  A. Bahremand,et al.  Distributed Hydrological Modeling and Sensitivity Analysis in Torysa Watershed, Slovakia , 2008 .