The importance of this study lies in the hydrological analysis of the
relationship between the drainage system and the precipitation. The
problem of the study reveals in the water incompetence in the basin
which get to 336 million m3 and will gets
to 600 million m3 with
probability (p = 50%) and associated with missing the accurate evaluation
of the water resources.
The study aims to evaluate the water resources in the basin, to create a
mathematical model for calculation of the run off and its relationship with
the precipitation, and to predict the water resources for the hydrological
year according to many probabilities and the complementary
management for water resources.
Stages of the study involve the following:
1- Determining of the active stations in the feeding of hydrometric
stations.
2- Observing the average of water resources quantity (precipitation) over
the basin that reached 9764 million m3 and range between 2,26millionm3 in the hydrometric station of AL- Hamidi and 112,42
million m3 in Hama station.
3- Observing the average of the run off in the basin reached 744,67542
million m3.
4- Observing the run off system in the basin is a snow-rain system, where
the average of maximum discharge associates with precipitation and the
minor discharge associates with deprivation of the precipitation.
5- Creating a mathematical model for calculation of the run off and its
relationship with the precipitation.
6- Predicting the water resources for the hydrological year according to
many probabilities.
7- Creating a water strategy for the basin according to the data of water
predication in many probabilities
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.
