Rainfall is considered as one of the most difficult and complex elements of the
hydrological cycle, to understand and model, due to the complexity of air operations that
generate rain. The importance of research comes from the direct relationship b
etween the
rainfall amount and economic & social activities of the population, planning scopes of the
water resources management, particularly with respect to the agricultural development.
The research aims to highlight the rainfall amounts in Tartous station which is
located in the southern part of the Syrian coast, and applying one model of Box-Jenkins
models for the purpose of predicting future rainfall amounts. Multiple Arima models have
been tested. The results showed that the model SARIMA (3,0,4) was the best one. Data
were divided into 43 years to build the model and eight years to test it. The test results
gave high accuracy in the performance, and the model was used to predict the values of
annual rainfall for the next twenty years.
The study is carried on 65 pregnant patients attending the outpatient clinics and
inpatient department of Obstetrics and Gynecology of Al Assad University Hospital from
February- 2013 until February- 2014. They were divided to three groups. The fir
st is
preterm labor with intact membranes (25 patients). The second is PROM (20 patients). The
third one is control group (20 patients). All of them were submitted to ultrasonography to
find cervical changes (cervical canal length and diameter of internal os in order to predict
preterm delivery. Cervical canal length has a sensitivity of 91.43%, a specificity of 100%,
a positive predictive value of 100%, a negative predictive value of 76.92%, and a relative
risk (95% CI) of 4.33 (1.61-11.69) among patients with short cervical canal length and
those with normal cervix.
Diameter of internal os as a predictor of preterm delivery has a sensitivity of 60%, a
specificity of 60%, a positive predictive value of 84%, a negative predictive value of 30%,
and a relative risk (95% CI) of 1.2 (0.86–1.68).
Predicting crop yield response to irrigation level is increasingly important to
optimize irrigation under limited available water and for enhancing
sustainability and profitable production. This study was carried out to evaluate
the performance of
CropWat model in predicting deficit irrigation effect on
cotton crop, and to explore some alternatives for cotton irrigation. Crop yield
and water use data were collected from a 3-yr (2007-2009) field experiment to
assess the response of drip-irrigated cotton to deficit irrigation (DI).
Evapotranspiration is an important component of the
hydrologic cycle, and the accurate prediction of this parameter is
very important for many water resources applications. Thus, the
aim of this study is prediction of monthly reference
evapotranspiration using Artificial Neural Networks (ANNs) and
fuzzy inference system (FIS).
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).
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.
The study and design of water dams depend essential on prediction of water volumes
or future predicted in rivers, by using the time series analysis of the historical
measurements.
The research aims to make statistical study of monthly water volume
s incoming in
AL-Aroos River in Syrian coastal and future prediction of these volumes. And the
Box-Jenkins models is adopt to analysis the time series data, because of its high accuracy.
We attend the monthly water volumes for 15 years. And after doing the wanted tests on
model residuals we found that the best model to represent the data is SARIMA(0,1,2)
(1,2,1)12 , and after dividing the data to 14 years to build the model and one year to test it ,
and depending on the smallest of weighted mean of criteria RMSE, MAP, MAE,. The best
predicted model is SARIMA (1,1,0) (0,1,1)12 and the model give the nearest predicted of
measured data actually.
Evaporation is a major meteorological component of the hydrologic cycle, and it
plays an influential role in the development and management of water resources. The aim
of this study is to predict of the monthly pan evaporation in Homs meteostation
using
Artificial Neural Networks (ANNs), which based on monthly air temperature and relative
humidity data only as inputs, and monthly pan evaporation as output of the network. The
network was trained and verified using a back-propagation algorithm with different
learning methods, number of processing elements in the hidden layer(s), and the number of
hidden layers. Results shown good ability of (2-10-1) ANN to predict of monthly pan
evaporation with total correlation coefficient equals 96.786 % and root mean square error
equals 24.52 mm/month for the total data set. This study recommends using the artificial
neural networks approach to identify the most effective parameters to predict evaporation.
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.
In this work, we compare three different modeling approaches for the scores of soccer matches with regard to their predictive performances based on all matches from the four previous FIFA World Cups 2002 – 2014: Poisson regression models, random
forests and ranking methods.
