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Rainfall-Runoff Modeling by Using Hybrid System of Artificial Neural Network and Genetic Algorithm

نمذجة علاقة الهطل_الجريان باستخدام نظام هجين من الشبكات العصبية الصنعية و الخوارزمية الجينية

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 Publication date 2017
and research's language is العربية
 Created by Shamra Editor




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This study has reached to that ANN (5-9-1) (five neurons in input layer_nine neurons in hidden layer _ one neuron in output layer) is the optimum artificial network that hybrid system has reached to it with mean squared error equals (1*10^-4) (0.7 m3/sec), where this software has summed up millions of experiments in one step and in limited time, it has also given a zero value of a number of network connections, such as some connections related of relative humidity input because of the lake of impact this parameter on the runoff when other parameters are avaliable. This study recommend to use this technique in forecasting of evaporation and other climatic elements.



References used
AWAD, A. ؛POSER, I. 2007-Calibrating Conceptual Rainfall- Runoff Models Using a Real Genetic Algorithm Combined with a Local Search Method, Vol. 1, 174-181
Mutlu, E; Chaubey, I; Hexmoor, H; Bajwa, S. 2008- Comparison of artificial neural network models for hydrologic predictions at multiple gauging stations in an agricultural watershed, Published online in Wiley InterScience, 1-10
ASADI, S.؛ SHAHRABI, J.؛ ABBASZADEH, P. ؛TABANMEHR, S. 2013- A new hybrid artificial neural networks for rainfall_runoff process modeling, Neurocomputing an international journal, Iran, 470_480
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The relationship between precipitation and surface runoff is one of the fundamental components of the hydrological cycle of water in nature and is one of the most complex and difficult to understand because of the large number of parameters involv ed in the modeling of physical processes and the breadth of parmetry and temporary change in basin specifications. Multiple rainfall models Modeling the relationship between precipitation and runoff is very important for engineering design and integrated water resources management, as well as flood forecasting and risk prevention.
The relation between rainfall and runoff forms one of the main hydrological cycle elements. It is one of the most complex hydrological phenomena because of the great numbers of parameters used in modeling the physical processes, the expansion of thei r parameter space, and the temporary change in watershed specifications. Thus, modeling the relation between rainfall and runoff is necessary for hydrological and hydraulic engineering design, integrated management of water resourses, and forecasting flood and preventing its dangers. This research aims at modeling the relation between rainfall and runoff in Alkabeer Aljononbee catchment. It depends on the technique of Artificial Neural Network (ANN). The mathematical model was built by the ntstool and nntool available in the Matlab program. This model depends on daily rainfall, evaporation, air temperature, and relative humidity data taken from meteorological stations that are distributed in the watershed. The daily runoff data have also been used for checking the performance accuracy of the network, using the Simulink technique. The results of this research confirm that artificial neural network technology offers good results in modeling the relation rainfall-runoff, depending on the set of data used. So it could be a better alternative than traditional approaches.
the aim of this study is determination of the most influential climatic factors in the rainfall runoff relationship in Al-Kabir Al-shimalee river using artificial neural networks. The inputs included Precipitation, runoff, in different delays, in addition on لاclimate factor in each network, to determinate the best model.
The principal objective of this research is an adoption of the Genetic Algorithm (GA) for studying it firstly, and to stop over the operations which are introduced from the genetic algorithm.The candidate field for applying the operations of the g enetic algorithm is the sound data compression field. This research uses the operations of the genetic algorithm for the enhancement of the performance of one of the popular compression method. Vector Quantization (VQ) method is selected in this work. After studying this method, new proposed algorithm for mixing the (GA) with this method was constructed and then the required programs for testing this algorithm was written. A good enhancement was recorded for the performance of the (VQ) method when mixed with the (GA). The proposed algorithm was tested by applying it on some sound data files. Some fidelity measures are calculated to evaluate the performance of the new proposed algorithm.
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