River flows depend on precipitation in their catchments, where the flow is highly
correlated with precipitation, among many climatic and geographic factors. The
relationship between precipitation and runoff is of great importance in estimating flow
changes in The HWAIZ basin that is located between The-Zrod and The-Gelani basins.
The Al-HWAIZ Dam was built on the HWAIZ River with storage capacity of 16.5 MCM.
The purpose of this study is to find a relationship between rainfall and runoff in The
HWAIZ basin. This study depended on statistical analysis of rainfall and runoff data, and
the analytical study of the annual rainfall data (1959-2011), to guess the trend of rainfall
and its future changes and forecasting changes in the HWAIZ river flows. The study
showed that the runoff coefficient values ranged between (0.007-0.66). A mathematical
relationship was established that allows to estimate flow based on measured or predicted
precipitation values, as well as appraise missing or lacking data with accepted level of
accuracy.
The Alsafarqieh watershed is located on the western slopes of the coastal mountain range,
Its area is 132.58 km2, It forms a part of the Alros river basin, The river starts at a height of
1200 m, A group of tributaries meet and form the Alros River
, which flows into the
Mediterranean Sea. Salaheddin Dam was constructed to store 10 MCM on the riverbed at
the intersection of the Qurdaha River with the Shehada River. The study aims to determine
the rainfall- runoff relationship in The Alsafarqieh watershed. The solution depends on the
statistical analysis of precipitation and runoff data. Then the study found the mean annual
precipitation is 159.6 MCM/year, and the mean annual flow into the Salaheddin lake was
9.4 MCM during the study period (2010-2012), so the runoff coefficient is 0.06. This
indicates a significant water loss. A mathematical equation to predict the runoff quantities
depending on the values of precipitation, has been concluded. This is important to study
water projects for water storage and flood prevention.
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 .
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.
the aim of this study is
determination of the most influential climatic factors in the rainfall
runoff relationship in Al-Kabir Al-shimalee river using artificial
neural networks. The inputs included Precipitation, runoff, in
different delays, in
addition on لاclimate factor in each network, to
determinate the best model.
Rainfall is highly non-linear and complicated phenomena, which require nonlinear
mathematical modeling and simulation for accurate prediction. This study
comparing the performance of the prediction of one-day-ahead, where Two
Feed Forward Neural N
etwork FFNN models were developed and implemented
to predict the rainfall on daily for three months (December, January, February).
These models are Artificial Neural Network traditional (ANN) model and artificial
neural network technique combined with wavelet decomposition (Wavelet-
Neural) According to two different methods to build a model using two types of
wavelets of Daubechies family (db2, db5). In order to compare the performance
of the models in their ability to predict the rains on short-term (for one and two
and three-days-ahead) the last months of the period of study, used some statistical
standards, These parameters include the Root Mean Square Error RMSE,
Coefficient Of Correlation (R).
Weather forecasting (especially rainfall) is one of the most important and challenging
operational tasks carried out by meteorological services all over the world. Itis furthermore
a complicated procedure that requires multiple specialized fields o
f expertise.
In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform
is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of
Homs Meteorological Station on accounts of the preceding events of rainfall data.
The feed-forward neural network with back-propagation Algorithm is used in the
learning and forecasting, where the time series of rain that detailed transactions and the
approximate three levels of analysis using a Discrete wavelet transform (DWT).
The study found that the neural network WNN structured )5-8-8-8-1(, able to predict
the monthly rainfall in Homs station on the long-term correlation of determination and root
mean squared-errors (0.98, 7.74mm), respectively.
Wavelet Transform technique provides a useful feature based on the analysis of the
data, which improves the performance of the model and applied this technique in ANNmodels
for rain because it is simple, as this technique can be applied to other models.
Due to the importance of water, and the increasing of demand at the present time due
to the tremendous development in all spheres of economic and social life, and as the
evaluation, planning and management of water sources, one of the important top
ics in
human life, especially in areas with scarce rainfall or where rainfall distribution is poor or
irregular so cannot be used for different purposes.
From here, the importance of the research in forecasting rainfall in the Husn
Suleiman station, comes, and to achieve this goal the data of time series for the average
annual rainfall precipitation been used in Husn Suleiman station which located in the
province of Tartous on longitude 36 ° 15 ' andlatitude 34 ° 56', for the period between
1959-2011,
The methodology of "Box – Jenkins" been used in the study, this methodology
relies on finding future forecasts from original data series.
Also,the applications “MINITAB, EXCEL” have been used in the statistical side and
the preparation of the study results.
As a result, the study found that rainfall value in the 'Husn Suleiman station'
decreasing, this decline amounted to 3.7 mm per year during the monitoring period.
Also, the appropriate (ARIMA) model for the series was build after it passed the
various statistical tests are required, and founded that ARIMA(1,0,0) model is a good
representation of the data and the ARIMA(4,1,5) model is the right model to forecast
future rainfall.
This research was conducted in order to determine the impact
of raindrops in terms of force of impact and its relation to rain
intensity as well as the relay rain on the amount of soil eroded
and water drifting due to water erosion .
This research aims at finding the model of rainfall carnal slope
in Kastal Mouaf-Coastal basin . Statistical analysis has been
performed during 48 year ( 1961-2009) using spss v.22 to
represent the relationship between the rainfall and the factors
whose impacts are shown by the passage time .
