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.
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.
Evaporation forms one of the hydrology cycle elements that it's hard to measure its actual amounts in the field conditions, so it’s estimated by calculations of experimental relations, which depend on climatic elements data. So the research goal is t
o build a mathematical model to estimate monthly evaporation amount in plain area of Syrian Coast, using Artificial Neural Network (ANN), and depending on dry air temperature, and produce comparison study between the results of network and other models. The mathematical model was built by the (NN-tool box), which is one of the v tools. A multilayer ANN architecture of error Back-propagation algorithm was built. The suitable training algorithms, number of hidden layers, number of neurons in each hidden layer, were determined.
The results showed that the ANN (1-9-1) was the best model with MSE of 0.0032 for validation group, using Transfer Function Logsigmoid and Linear in hidden and output layers, respectively.
A comparison model for the results obtained from the proposed ANN with EVANOV model by using SIMULINK technique was developed. This indicated that the ANN using temperature only gives results more accurate than EVANOV equation in determining evaporation.
Evapotranspiration forms one of the hydrology cycle elements that it's hard to
measure its actual amounts in the field conditions, so it’s estimated by calculations of
experimental relations that depend on climatic elements data. These estimations
include
different errors because of approximation processes. The research goals to accurate
estimation of the monthly reference evapotranspiration amount in Safita area (on the east
coast of the Mediterranean Sea), and the research depends on the technique of Artificial
Neural Network (ANN), and the mathematical model was built by the
(nftool), which is one of the Matlab tools, depending on monthly air temperature and
relative humidity data which were taken from Safita meteorological station, and the data of
monthly pan evaporation (Class A pan) has been used, after modifying its results, for the
purpose of checking the performance accuracy of the network, by using Simulink
technique, which is existing in Matlab Programs Package. The results of the research
verify that a multi-layer ANN of error Back-propagation algorithm gives a good result in
estimating monthly reference Evapo-transpiration for the used data group.
The stability analysis of coastal structure is very important because it involves many
design parameter s to be considered for the save and economical design of structure.
In the present study neural network technique is adopted to predict the stab
ility
number of rubble mound breakwater.
One model is constructed based on the parameters which influence on the stability of
rubble mound breakwater, the back propagation algorithm is used in training network .
Agood correlation is obtained between network predicted stabilityand estimated
ones.
Correlation coefficient=0.88.
This study is aiming at building a mathematical model to estimate evaporation from Mountainous region in Syrian Coast, using an artificial neural network, based on four metrological parameters (i.e. temperature, relative humidity, wind speed and sun
hours), then studying the effect of adding time variable on evaporation estimation. The mathematical model was built by the (NN-tool box), which is one of the MATLAB tools, using the daily value of the above mentioned parameters in addition to time, as the network inputs and the evaporation measured from the American pan class A as the network output . The results show that ANN4+T model which have 5 inputs (temperature, relative humidity, wind speed, sun hours, time) is the best in estimation evaporation with correlation factor of 0.8919 and Mean square error of 0.02166 for the validation set where the correlation factor in ANN4 (without time) was 0.8324 and MSE of 0.0327for the validation set.
Epilepsy is a chronic neurological disorder that occurs in the brain،
and affects approximately 2% of people around the world، where
epilepsy patients face a lot of difficulties in everyday life due to the
occurrence of seizures. Electroencephalog
ram (EEG) is used in
the automated detection of epileptic seizures، which has
Characteristics of non-linear and non-stationary. In this research،
we conducted automated detection of the seizures from the scalp
EEG signals using a Level 5 Discrete Wavelet Transforms DWT to
analyze the signal and extracting statistical features (maximum،
minimum، mean، average ، standard deviation، the ratio between
the mean values) and Categorizing using artificial neural networks
ANN for classification. The suggested detection method has
89.85% detection accuracy with 90.60% sensitivity ، and 89.1%
specificity.
Evapotranspiration is an important component of the
hydrologic cycle, and the accurate prediction of this parameter is
very important for many water resources applications. Thus, the
aim of this study is prediction of monthly reference
evapotranspiration using Artificial Neural Networks (ANNs) and
fuzzy inference system (FIS).
This study includes the possibility of using Artificial neural
networks (ANNs) with back-propagation algorithm in a short-term
prediction of water level in Qattinah Lake. The data used are the
water level data in the lake and rainfall data for the period from
1/5/2007 to 28/2/2005. 2009).
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.
