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.
The research has been conducted in different sites in lattakia, Alhamra and alsabahia
villages of Rabea and Aen alzarqa of Mashqeta. 14 cases of precipitation was marked
which caused runoff and water erosion of soil during the research (seasonal ra
infall in
2011/2012). Samples of rain water of every precipitation case were collected. by using rain
gauges that were installed in the research sites. After that, samples were moved to the
laboratory. The pH was determined after that the samples were analyzed by using an ion
chromatography device(IC).
The study shows the factors affecting rain precipitation, general
rate and annual, monthly and daily changes by calculating the
standard deviation and the annual fluctuation. The standard
deviation from the general average shows large values in th
e
stations located in the north of the study area. Stations in the
center and south, because of the nature of the dry climate, as well
as that the increase in the number of rainy days does not
necessarily mean an increase in the amount of precipitation, and a
difference in the amount of rainfall from one station to another
because of the difference in climatic factors affecting them .
The aim of this study is to determine the best probability distribution of annual,
monthly, annual one day maximum precipitation for stations in Aleppo Governorate by using nw2 test, then estimating annual, monthly, one day maximum precipitation to various return periods according to the best probability distribution, and using Chow’s general frequency formula.
