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Register a new userModeling Of The relationship between Rainfall,Runoff by using Artificial Neural Network
نمذجة علاقة الهطول المطري_الجريان السطحي باستخدام الشبكة العصبية الصنعية
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تشكل العلاقة بين الهطول المطري_الجريان السطحي إحدى المركبات الأساسية للدورة الهيدرولوجية للمياه في الطبيعة، كما أنها تشكل واحدة من أكثر الظواهر الهيدرولوجية تعقيداً و صعوبةً في الفهم؛ و ذلك بسبب كثرة عدد المتغيرات المتضمَّنة في نمذجة العمليات الفيزيائية و التغير المؤقت في مواصفات الحوض، إضافةً إلى تعدد نماذج الهطولات المطرية. و تعدُّ نمذجة العلاقة بين الهطول المطري و الجريان السطحي مهمة جدّاً من أجل التصميم الهندسي و الإدارة المتكاملة للموارد المائية، إضافةً إلى التنبؤ بالفيضان و درء مخاطره. من هنا تبرز أهمية نمذجة العلاقة بين الهطول المطري_الجريان السطحي اعتمادا على عدد من المتغيرات التي تؤثر بشكل فعال على الجريان السطحي، بما يتطلبه الأمر من الحفاظ على هذه الثروة الحيوية.
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 involved 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.
References used
PITTAMS, R. An Empirical Relationship Between Rainfall and Runoff, Journal of Hydrology New Zealand, Vol. 24, No . 2, 1970, 357-372
DAWSON, C. ؛ WILBY, R. An artificial neural network approach to rainfallrunoff modeling, Hydrological Sciences— Journal—des Sciences Hydrolo U.K. Vol.43, NO.1, 1998, 47-66
Arslan, C. Rainfall–Runoff Modeling Based on Artificial Neural Networks (ANNs). European Journal of Scientific Research U. K. Vol. 65, No. 4, 2011, 490-506
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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.
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 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.
In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investi
gated whether hierarchical structures make LMs more human-like, and if so, which parsing strategy is most cognitively plausible. In order to address this question, we evaluated three LMs against human reading times in Japanese with head-final left-branching structures: Long Short-Term Memory (LSTM) as a sequential model and Recurrent Neural Network Grammars (RNNGs) with top-down and left-corner parsing strategies as hierarchical models. Our computational modeling demonstrated that left-corner RNNGs outperformed top-down RNNGs and LSTM, suggesting that hierarchical and left-corner architectures are more cognitively plausible than top-down or sequential architectures. In addition, the relationships between the cognitive plausibility and (i) perplexity, (ii) parsing, and (iii) beam size will also be discussed.
Weather forecasting (especially rainfall) is one of the most important and challenging
operational tasks carried out by meteorological services all over the world. Itis furthermore
a complicated procedure that requires multiple specialized fields o
f expertise.
In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform
is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of
Homs Meteorological Station on accounts of the preceding events of rainfall data.
The feed-forward neural network with back-propagation Algorithm is used in the
learning and forecasting, where the time series of rain that detailed transactions and the
approximate three levels of analysis using a Discrete wavelet transform (DWT).
The study found that the neural network WNN structured )5-8-8-8-1(, able to predict
the monthly rainfall in Homs station on the long-term correlation of determination and root
mean squared-errors (0.98, 7.74mm), respectively.
Wavelet Transform technique provides a useful feature based on the analysis of the
data, which improves the performance of the model and applied this technique in ANNmodels
for rain because it is simple, as this technique can be applied to other models.
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