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Register a new userRainfall-Runoff Modelling by Artificial Neural Network in Alkabeer Aljonobee Catchment
نمذجة الهطول المطري_الجريان النهري باستخدام الشبكة العصبونية الصنعية في حوض نهر الكبير الجنوبي
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تُشكِّل العلاقة بين الهطول المطري_الجريان النهري Rainfall_Runoff (R_R إحدى المركبات الأساسية لدورة المياه في الطبيعة، كما أنها تُشكّل واحدة من أكثر الظواهر الهيدرولوجية تعقيداً و صعوبةً في الفهم؛ و ذلك بسبب كثرة عدد البارامترات المتضمَّنة في نمذجة العمليات الفيزيائية و بسبب اتساع فضائها البارامتري و التغير المؤقت في مواصفات الحوض، إضافةً إلى تعدد نماذج الهطولات المطرية. هذا و تعدُّ نمذجة العلاقة بين الهطول المطري _الجريان النهري مهمة جدّاً من أجل التصميم الهندسي و الإدارة المتكاملة للموارد المائية، إضافةً إلى التنبؤ بالفيضان و درء مخاطره. حيث يهدف هذا البحث إلى نمذجة العلاقة بين الهطول المطري_الجريان النهري في حوض نهر الكبير الجنوبي في سوريا، بالاعتماد على تقانة الشبكة العصبونية الصنعية (ANN) Artificial Neural Network، حيث بُني النموذج الرياضي باستخدام كلٍّ من nntool وntstool مكتبتين ملحقتين ببرنامج الماتلاب، و اعتمد النموذج على البيانات اليومية للهطول المطري، درجة حرارة الهواء، الرطوبة النسبية و التبخر في المحطات المناخية المنتشرة في الحوض، كما استُخدِمت بيانات الجريان النهري اليومية لغرض التحقق من صحة أداء الشبكة باستخدام تقانة Simulink المتاحة في حزمة برمجيات الماتلاب. أثبتت نتائج الدراسة أنَّ تقانة الشبكة العصبونية الصنعيَّة تعطي نتائج جيدة في نمذجة العلاقة بين الهطول المطري_الجريان النهري، اعتماداً على مجموعة البيانات المستخدَمة، و بالتالي يمكن اعتبارها بديلاً للطرائق التقليدية في نمذجة العلاقة R_R.
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 their 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.
References used
Rientjes, T. H. M.؛ De Vos, N. J. Constraints of artificial neural networks for rainfallrunoff modelling: trade-offs in hydrological state representation and model evaluation. Hydrology and Earth System Sciences Discussions Belgium, 2005, 365– 415
Abbott, M. B.؛ Bathurst, J. C.؛ Cunge, J. A.؛ O’Connell, P. E.؛ Rasmussen, J. An introduction to the european hydrological system – Syste`me Hydrologique Europe´en, “SHE”, 2: Structure of a physically-based, distributed modelling system. 1986, 61–77
Nash, J. E.؛ Sutcliffe, J. V. River flow forecasting through conceptual models. Journal of Hydrology1970, 282–290
Modarres, R. Multi-criteria validation of artificial neural network rainfall-runoff modeling. Hydrologu & Earth System Science Iran, 2009, 411-421
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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.
Accurately modeling rainfall-runoff (R-R) transform remains a challenging task despite that a wide range of modeling, either knowledge-driven or data-driven. knowledge-driven models need a large amount of parameters, so it suffers from plenty numbers
of parameters, for this reason the hydrologists start looking for a simple modeling methods, that need a few parameters such as data _driven methods, so The present study amis to use artificial neural network, which is one type of this methods for modeling the relationship between rainfall and runoff in Alkabeer Aljanonbee river catchment in Tartous City. Elman Neural Network is depended on for prediction of runoff by testing twenty four models have different architectures. So all models have been tested by using different numbers of neurons in the hidden layer, by using nntool book, which is available in the Matlab program.
The results of the research verify that the model which has each of temperature, relative humidity, evaporation and rainfall in the input layer with time delay equal to three days (0:-3), in addation to preveous value of runoff (-1:-3), gives a best performance for used data with mean square error equal to 2.8*10^-5, and correlation coefficient 0.96. So it has been reached that Elman network technology gives a good results in modeling the relation rainfall_runoff So it could be a good alternative instead of traditional approaches.
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.
The Alsafarqieh watershed is located on the western slopes of the coastal mountain range,
Its area is 132.58 km2, It forms a part of the Alros river basin, The river starts at a height of
1200 m, A group of tributaries meet and form the Alros River
, which flows into the
Mediterranean Sea. Salaheddin Dam was constructed to store 10 MCM on the riverbed at
the intersection of the Qurdaha River with the Shehada River. The study aims to determine
the rainfall- runoff relationship in The Alsafarqieh watershed. The solution depends on the
statistical analysis of precipitation and runoff data. Then the study found the mean annual
precipitation is 159.6 MCM/year, and the mean annual flow into the Salaheddin lake was
9.4 MCM during the study period (2010-2012), so the runoff coefficient is 0.06. This
indicates a significant water loss. A mathematical equation to predict the runoff quantities
depending on the values of precipitation, has been concluded. This is important to study
water projects for water storage and flood prevention.
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.
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