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.
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.
This study has been done to develop scientific research and select talented people of
post-graduate students (master students) to continue and get doctoral degrees (degree in
PHD) at Tishreen University. The research has been prepared, which aims t
o suggest a
model for measuring the degree of creativity and talent for post-graduate students by using
one of artificial intelligence techniques such as Fuzzy Logic.
An expert system has been built that contains an inference rule which consists of
three types of tests: (Theory Test, TT), (Practice Test, PT), and (Creativity Test, CT) for
each course.
This intelligent system has also aimed to determine the ability to make decisions
which gives the rate of talent for post- graduate students.
The study has reached to an important set of results, and the most important is:
Results have shown high strength and reliability shows the validity of this proposed
model, the validity of the results have reached to 85% and 100%, by using two different
methods to defuzzificate of proposed model.
The evaporation is one of the basic components of the hydrologic cycle and it is
essential for studies such as water balance, irrigation system design and water resource
management, and it requires knowledge of many climatic variables. Although, th
ere are
many empirical formulas available for evaporation estimate, but their performances are not
all satisfactory due to the complicated nature of the evaporation process. Accordingly, this
paper is an attempt to assess the potential and usefulness of ANN based modeling for
evaporation prediction from HAMA by using temperature, relative humidity and wind
velocity. The mathematical model was built by the (nntool-box), which is one of the
MATLAB tools. The feed forward back propagation network with one hidden layer has
been utilised to construct the model. Different networks with different number of neurons
were evaluated. Root Mean Squared Error (RMSE) was employed to evaluate the accuracy
of the proposed model. The study shows that ANN (3-14-1) was the best model with
RMSE (21.5mm/month) and R2 (0.97).
This study suggests using other types of neural networks for estimation of
evaporation
