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.
